Распознавание именованных сущностей

The identification of unknown proper names in text is a significant challenge for NLP systems operating on unrestricted text. A system which indexes documents according to name references can be …...

All of BBN's research under the TIPSTER III program has focused on doing extraction by applying statistical models trained on annotated data, rather than by using programs that execute hand-written …...

Identifying and classifying personal, geographic, in- stitutional or other names in a text is an impor- tant task for numerous applications. This paper de- scribes and evaluates a language-independent boot- …...

It is often claimed that Named En- tity recognition systems need extensive gazetteers--lists of names of people, or- ganisations, locations, and other named entities. Indeed, the compilation of such gazetteers …...

This paper describes experiments to establish the performance of a named entity recognition system which builds categorized lists of names from manu- ally annotated training data. Names in text are …...

In this paper we describe a technique for improving the performance of an information extraction system for speech data by explicitly modeling the errors in the recognizer output. The approach …...

We present two methods for learning the struc- ture of personal names from unlabeled data. The first simply uses a few implicit constraints governing this structure to gain a toehold …...

This paper presents a method that as- sists in maintaining a rule-based named-entity recognition and classifi- cation system. The underlying idea is to use a separate system, constructed with the …...

Chinese NE (Named Entity) recognition is a difficult problem because of the uncertainty in word segmentation and flexibility in language structure. This paper proposes the use of a rationality model …...

We present an architecture for the integra- tion of shallow and deep NLP components which is aimed at flexible combination of different language technologies for a range of practical current …...

Named Entity (NE) recognition is a task in which proper nouns and numerical information are ex- tracted from documents and are classified into cat- egories such as person, organization, and …...

This paper investigates the use of a language inde- pendent model for named entity recognition based on iterative learning in a co-training fashion, using word-internal and contextual information as inde- …...

The regularity of named entities is used to learn names and to extract named entities. Having only a few name elements and a set of patterns the al- gorithm learns …...

This paper proposes a Hidden Markov Model (HMM) and an HMM-based chunk tagger, from which a named entity (NE) recognition (NER) system is built to recognize and classify names, times …...

This paper describes algorithms which rerank the top N hypotheses from a maximum-entropy tagger, the applica- tion being the recovery of named-entity boundaries in a corpus of web data. The …...

This paper describes a robust linear classifica- tion system for Named Entity Recognition. A similar system has been applied to the CoNLL text chunking shared task with state of the …...

This paper investigates stacking and voting methods for combining strong classifiers like boosting, SVM, and TBL, on the named-entity recognition task. We demonstrate several ef- fective approaches, culminating in a …...

Here we present work on using spatial knowl- edge in conjunction with information extrac- tion (IE). Considerable volume of location data was imported in a knowledge base (KB) with entities …...

This paper presents a Chinese word segmen- tation system that uses improved source- channel models of Chinese sentence genera- tion. Chinese words are defined as one of the following four …...

Discriminative models have been of inter- est in the NLP community in recent years. Previous research has shown that they are advantageous over generative mod- els. In this paper, we …...

This paper presents a maximum entropy based Chinese named entity recognizer (NER): Mencius. It aims to address Chinese NER problems by combining the advantages of rule-based and machine learning (ML) …...

This work studies Named Entity Recog- nition (NER) for Catalan without mak- ing use of annotated resources of this language. The approach presented is based on machine learning techniques and …...

This paper presents a classifier-combination experimental framework for named entity recognition in which four diverse classi- fiers (robust linear classifier, maximum en- tropy, transformation-based learning, and hid- den Markov model) …...

We present a named entity recognition and classification system that uses only probabilis- tic character-level features. Classifications by multiple orthographic tries are combined in a hidden Markov model framework to …...

We discuss two named-entity recognition mod- els which use characters and character � -grams either exclusively or as an important part of their data representation. The first model is a …...

In this approach to named entity recognition, a recurrent neural network, known as Long Short-Term Memory, is applied. The network is trained to perform 2 passes on each sentence, outputting …...

This paper presents a multilingual system designed to recognize named entities in a wide variety of languages (currently more than 12 languages are concerned). The system includes original strategies to …...

We present a question answering system that combines information at the lexical, syntactic, and semantic levels, in the process to find and rank the candidate answer sentences. The can- didate …...

We report on the results of an experiment aimed at enabling a machine translation system to select the appropriate strategy for dealing with words and phrases which have different translations …...

In this paper, we propose a multi-criteria- based active learning approach and effec- tively apply it to named entity recognition. Active learning targets to minimize the human annotation efforts by …...

In this paper we describe a way to discover Named Entities by using the distribution of words in news articles. Named Entity recog- nition is an important task for today’s …...

This work applies boosted wrapper induction (BWI), a machine learning algorithm for informa- tion extraction from semi-structured documents, to the problem of named entity recognition. The de- fault feature set …...

The recognition of named entities is now a well- developed area, with a range of symbolic and machine learning techniques that deliver high accuracy identification and categorisation of a variety …...

There has been little prior work on Named Entity Recognition for ”informal” docu- ments like email. We present two meth- ods for improving performance of per- son name recognizers for …...

. One of issues in the bootstrapping for named entity recogni- tion is how to control annotation errors introduced at every iteration. In this paper, we present several heuristics for …...

We report on an active learning experi- ment for named entity recognition in the astronomy domain. Active learning has been shown to reduce the amount of la- belled data required …...

Traditional approaches to Information Ex- traction (IE) from speech input simply consist in applying text based methods to the output of an Automatic Speech Recog- nition (ASR) system. If it …...

Named Entity Recognition (NER) is a fundamental task in text mining and nat- ural language understanding. Current ap- proaches to NER (mostly based on super- vised learning) perform well on …...

Named-entity recognition systems extract entities such as people, organizations, and locations from unstructured text. Rather than extract these mentions in isolation, this paper presents a record extraction sys- tem that …...

We present a system for named entity recognition (ner) in astronomy jour- nal articles. We have developed this system on a ne corpus comprising ap- proximately 200,000 words of text …...

Deidentification of clinical records is a crucial step before these records can be distributed to non-hospital researchers. Most approaches to deidentification rely heavily on dictionaries and heuristic rules; these approaches …...

Named Entity recognition (NER) is an important part of many natural language processing tasks. Current approaches of- ten employ machine learning techniques and require supervised data. However, many languages lack …...

We cannot use non-local features with cur- rent major methods of sequence labeling such as CRFs due to concerns about com- plexity. We propose a new perceptron algo- rithm that …...

This paper presents a large-scale system for the recognition and semantic disambiguation of named entities based on information extracted from a large encyclopedic collection and Web search results. It describes …...

Question answering on speech transcripts (QAst) is a pilot track of the CLEF com- petition. In this paper we present our con- tribution to QAst, which is centred on a …...

Many errors produced by unsupervised and semi-supervised relation extraction (RE) systems occur because of wrong recogni- tion of entities that participate in the rela- tions. This is especially true for …...

. Named Entity Recognition (NER) is always limited by its lower recall resulting from the asymmetric data distribution where the NONE class dominates the entity classes. This paper presents an …...

We describe our effort in developing a Named Entity Recognition (NER) system for Hindi using Maximum Entropy (Max- Ent) approach. We developed a NER an- notated corpora for the purpose. …...

The Named Entity Recognition (NER) task has been garnering significant attention in NLP as it helps improve the performance of many natural language processing applica- tions. In this paper, we …...

We present an algorithmic framework for learning multiple related tasks. Our frame- work exploits a form of prior knowledge that relates the output spaces of these tasks. We present PAC …...

We present a novel hierarchical prior struc- ture for supervised transfer learning in named entity recognition, motivated by the common structure of feature spaces for this task across natural language …...

We present a novel learning framework for pipeline models aimed at improving the com- munication between consecutive stages in a pipeline. Our method exploits the confidence scores associated with outputs …...

In this paper, we describe a system by which the multilingual characteristics of Wikipedia can be utilized to annotate a large corpus of text with Named Entity Recognition (NER) tags …...

Active learning is a proven method for re- ducing the cost of creating the training sets that are necessary for statistical NLP. How- ever, there has been little work on …...

This paper introduces a new method for iden- tifying named-entity (NE) transliterations in bilingual corpora. Recent works have shown the advantage of discriminative approaches to transliteration: given two strings (ws, …...

Statistical machine learning methods are em- ployed to train a Named Entity Recognizer from annotated data. Methods like Maxi- mum Entropy and Conditional Random Fields make use of features for …...

We present an environment for the recog- nition and translation of Named Entities (NEs). The environment consists of a new formalism for the Named Entity Recognition and Translation (NERT), a …...

. The paper proposes an integrated framework for web personal information extraction, such as biographical information and occupation, and those kinds of information are necessary to further construct a social …...

Named entity recognition (NER) for En- glish typically involves one of three gold standards: MUC, CoNLL, or BBN, all created by costly manual annotation. Recent work has used Wikipedia to …...

Named Entity (NE) information is criti- cal for Information Extraction (IE) tasks. However, the cost of manually annotating sufficient data for training purposes, espe- cially for multiple languages, is prohibitive, …...

We propose a system which builds, in a semi-supervised manner, a resource that aims at helping a NER system to anno- tate corpus-specific named entities. This system is based on …...

Bootstrapping is the process of improving the performance of a trained classifier by iteratively adding data that is labeled by the classifier itself to the training set, and retraining the …...

The paper presents a feature-rich approach to the automatic recognition and categorization of named entities (persons, organizations, loca- tions, and miscellaneous) in news text for Bulgar- ian. We combine well-established …...

For many language technology applications, such as question answering, the overall sys- tem runs several independent processors over the data (such as a named entity recognizer, a coreference system, and …...

Named entity recognition for morpholog- ically rich, case-insensitive languages, in- cluding the majority of semitic languages, Iranian languages, and Indian languages, is inherently more difficult than its English counterpart. Worse …...

The ambiguity of person names in the Web has become a new area of interest for NLP researchers. This challenging problem has been formulated as the task of clustering Web …...

. In this paper, we introduce a minimally supervised method for learning to classify named-entity titles in a given encyclopedia into broad semantic categories in an existing ontology. Our main …...

AbstractNamed Entity Recognition is a relatively well-understood NLP task, with many publicly available training resources and software for processing English data. Other languages tend to be underserved in this area. …...

AbstractWe present a fully functional Arabic information extraction (IE) system that is used to analyze large volumes of news texts every day to extract the named entity (NE) types person, …...

AbstractIn this paper, we present a detailed and critical analysis of the behaviour of the CasEN named entity recognition system during the French Ester2 evaluation campaign. In this project, CasEN …...

AbstractWe have adapted and extended the automatic Multilingual, Interoperable Named Entity Lexicon approach to Arabic, using Arabic WordNet (AWN) and Arabic Wikipedia (AWK). First, we extract AWN’s instantiable nouns and …...

AbstractThis paper presents a novel system HENNA (Hybrid Person Name Analyzer) for identifying language origin and analyzing linguistic structures of person names. We conduct ME-based classification methods for the language …...

Perceptron training is widely applied in the natural language processing community for learning complex structured models. Like all structured prediction learning frameworks, the structured perceptron can be costly to train …...

Named-entity recognition (NER) is an impor- tant task required in a wide variety of ap- plications. While rule-based systems are ap- pealing due to their well-known “explainabil- ity,” most, if …...

AbstractThis paper presents the preliminary works to put online a French oral corpus and its transcription. This corpus is the Socio-Linguistic Survey in Orleans, realized in 1968. First, we numerized …...

AbstractThis paper presents the participation of FIDJI system to the Web Question-Answering evaluation campaign organized by Quaero in 2009. FIDJI is an open-domain question-answering system which combines syntactic information with …...

. In this paper, we report a multiobjective optimization (MOO) based technique to select the appropriate subset of votes per classifier in an ensemble system. We hypothesize that the reliability …...

One of the main obstacles to produc- ing high quality joint models is the lack of jointly annotated data. Joint model- ing of multiple natural language process- ing tasks outperforms …...

AbstractThe Quaero project organized a set of evaluations of Named Entity recognition systems in 2009. One of the sub-tasks consists in extracting citations from patents, i.e. references to other documents, …...

Research in named entity recognition and mention detection has typically involved a fairly small number of semantic classes, which may not be adequate if seman- tic class information is intended …...

AbstractIn this paper, we propose classifier ensemble selection for Named Entity Recognition (NER) as a single objective optimization problem. Thereafter, we develop a method based on genetic algorithm (GA) to …...

AbstractThe Quæro program that promotes research and industrial innovation on technologies for automatic analysis and classification of multimedia and multilingual documents. Within its context a set of evaluations of Named …...

AbstractConceptMapper is an open source tool we created for classifying mentions in an unstructured text document based on concept terminologies (dictionaries) and yielding named entities as output. It is implemented …...

Problems stemming from domain adaptation continue to plague the statistical natural lan- guage processing community. There has been continuing work trying to find general purpose algorithms to alleviate this problem. …...

AbstractThis paper introduces a new named entity corpus for Dutch. State-of-the-art named entity recognition systems require a substantial annotated corpus to be trained on. Such corpora exist for English, but …...

We propose a Named Entity (NE) recogni- tion method using rules acquired from un- labeled data. Rules are acquired from au- tomatically labeled data with an NE rec- ognizer. These …...

Recognition and translation of named entities (NEs) are two current research topics with regard to the proliferation of electronic documents exchanged through the Internet. The need to assimilate these documents …...

We present a named entity recognition (NER) system for extracting product attributes and values from listing titles. Information extrac- tion from short listing titles present a unique challenge, with the …...

As developers of a highly multilingual named entity recognition (NER) system, we face an evaluation resource bottleneck problem: we need evaluation data in many languages, the annotation should not be …...

. This paper presents first the CasEN transducer cascade to recognize French Named Entities. CasEN is implemented with the CasSys software of the Unitex plateform and is put at user …...

Automatically building domain-specific on- tologies is a highly challenging task as it re- quires extracting domain-specific terms from a corpus and assigning them relevant domain concept labels. In this paper, …...

This paper describes a new, freely available, highly multilingual named entity resource for person and organisation names that has been compiled over seven years of large-scale multilingual news analysis combined …...

This paper shows that the performance of history-based models can be significantly im- proved by performing lookahead in the state space when making each classification deci- sion. Instead of simply …...

We investigate how the automatic identi- fication of noun compounds and named entities can contribute to keyphrase ex- traction and we also show how previously identified noun compounds affect named …...

We use search engine results to address a par- ticularly difficult cross-domain language pro- cessing task, the adaptation of named entity recognition (NER) from news text to web queries. The …...

. In this paper, we present Thai named entity recognition (NER) systems using supervised Conditional Random Fields (CRFs) with various answer patterns to find out whether different answer patterns would …...

Named entity recognition (NER) has been extensively studied for the names of genes and gene products but there are few proposed solutions for phenotypes. Phenotype terms are expected to play …...

AbstractNamed entity recognition, which focuses on the identification of the span and type of named entity mentions in texts, has drawn the attention of the NLP community for a long …...

AbstractWe have developed a new OSGi-based platform for Named Entity Recognition (NER) which uses a voting strategy to combine the results produced by several existing NER systems (currently OpenNLP, LingPipe …...

AbstractThe European Commission's (EC) Directorate General for Translation, together with the EC's Joint Research Centre, is making available a large translation memory (TM; i.e. sentences and their professionally produced translations) …...

In this paper, we propose a differential evolution (DE) based two-stage evolutionary approach for named entity recognition (NER). The first stage concerns with the problem of relevant feature selection for …...

Many NLP tasks interact with syntax. The presence of a named entity span, for example, is often a clear indicator of a noun phrase in the parse tree, while a …...

Parallel corpora have applications in many ar- eas of Natural Language Processing, but are very expensive to produce. Much information can be gained from comparable texts, and we present an …...

This paper introduces a method to improve supervised word sense disambiguation perfor- mance by including a new class of features which leverage contextual information from large unannotated corpora. This new …...

Named Entity Recognition (NER) is a task which helps in finding out Persons name, Location names, Brand names, Abbreviations, Date, Time etc and classifies them into predefined different categories. NER …...

We consider the problem of NER in Arabic Wikipedia, a semisupervised domain adap- tation setting for which we have no labeled training data in the target domain. To fa- cilitate …...

AbstractNamed entity recognition of the clinical entities disorders, findings and body structures is needed for information extraction from unstructured text in health records. Clinical notes from a Swedish emergency unit …...

Online content analysis employs algorithmic methods to identify entities in unstructured text. Both machine learning and knowledge-base approaches lie at the foundation of contemporary named entities extraction systems. However, the …...

Although first names and nicknames in the United States have been well documented, there has been almost no quantitative analysis on the usage and association of these names amongst themselves. …...

Generic rule-based systems for Information Extraction (IE) have been shown to work reasonably well out-of-the-box, and achieve state-of-the-art accuracy with further domain customization. However, it is generally rec- ognized that …...

In this paper we deal with Named En- tity Recognition (NER) on transcriptions of French broadcast data. Two aspects make the task more difficult with respect to previ- ous NER …...

AbstractDue to the nature of complex NLP problems, structured prediction algorithms have been important modeling tools for a wide range of tasks. While there exists evidence showing that linear Structural …...

NLP models have many and sparse features, and regularization is key for balancing model overfitting versus underfitting. A recently re- popularized form of regularization is to gen- erate fake training …...

Feature and context aggregation play a large role in current NER systems, allowing significant opportunities for research into op- timizing these features to cater to different domains. This work strives …...

In this paper, we propose a new method for semantic class induction. First, we in- troduce a generative model of sentences, based on dependency trees and which takes into account …...

Mining Partial Annotation Rules for Named Entity Recognition During the last decades, the unremitting increase of numeric data available has led to a more and more urgent need for efficient …...

Some languages lack large knowledge bases and good discriminative features for Name Entity Recognition (NER) that can general- ize to previously unseen named entities. One such language is Arabic, which: …...

Different languages contain complementary cues about entities, which can be used to im- prove Named Entity Recognition (NER) sys- tems. We propose a method that formu- lates the problem of …...

We propose a novel approach to sentiment analysis for a low resource setting. The in- tuition behind this work is that sentiment expressed towards an entity, targeted senti- ment, may …...

AbstractWe present a joint model of three core tasks in the entity analysis stack: coreference resolution (within-document clustering), named entity recognition (coarse semantic typing), and entity linking (matching to Wikipedia …...

We experiment with using different sources of distant supervision to guide unsupervised and semi-supervised adaptation of part-of-speech (POS) and named entity taggers (NER) to Twitter. We show that a particularly …...

AbstractNamed entity recognition and disambiguation are of primary importance for extracting information and for populating knowledge bases. Detecting and classifying named entities has traditionally been taken on by the natural …...

AbstractThis paper describes an experiment to compare four tools to recognize named entities in Portuguese texts. The experiment was made over the HAREM corpora, a golden standard for named entities …...

AbstractIn this paper we investigate how different dependency representations of a treebank influence the accuracy of the dependency parser trained on this treebank and the impact on several parser applications: …...

This paper reports our submissions to the four subtasks of Aspect Based Sentimen- t Analysis (ABSA) task (i.e., task 4) in SemEval 2014 including aspect term ex- traction and aspect …...

AbstractIn this paper, we present our attempt at annotating procedural texts with a flow graph as a representation of understanding. The domain we focus on is cooking recipe. The flow …...

AbstractNamed Entity Recognition task needs high-quality and large-scale resources. In this paper, we present RENCO, a based-rules system focused on the recognition of entities in the Cosmetic domain (brandnames, product …...

AbstractNamed entity recognition (NER) is a knowledge-intensive information extraction task that is used for recognizing textual mentions of entities that belong to a predefined set of categories, such as locations, …...

This paper describes an approach for au- tomatic construction of dictionaries for Named Entity Recognition (NER) using large amounts of unlabeled data and a few seed examples. We use Canonical …...

Most state-of-the-art approaches for named-entity recognition (NER) use semi supervised information in the form of word clusters and lexicons. Recently neural network-based language models have been explored, as they as …...

AbstractThe Europe Media Monitor (EMM) is a fully-automatic system that analyses written online news by gathering articles in over 70 languages and by applying text analysis software for currently 21 …...

This paper describes our participation in task 7 of SemEval 2014, which focuses on analysis of clinical text. The task is divided into two parts: recognizing men- tions of concepts …...

AbstractVarious recent studies show that the performance of named entity recognition (NER) systems developed for well-formed text types drops significantly when applied to tweets. The only existing study for the …...

AbstractWe present SETimes.HR ― the first linguistically annotated corpus of Croatian that is freely available for all purposes. The corpus is built on top of the SETimes parallel corpus of …...

Recent years have seen increased interest in text normalization in social media, as the in- formal writing styles found in Twitter and other social media data often cause problems for …...

AbstractSupervised methods can achieve high performance on NLP tasks, such as Named Entity Recognition (NER), but new annotations are required for every new domain and/or genre change. This has motivated …...

Marketing materials such as flyers and other infographics are a vast online resource. In a number of industries, such as the com- mercial real estate industry, they are in fact …...

Learning a distinct representation for each sense of an ambiguous word could lead to more powerful and fine-grained mod- els of vector-space representations. Yet while ‘multi-sense’ methods have been proposed …...

Risk assessment is a crucial activity for financial institutions because it helps them to determine the amount of capi- tal they should hold to assure their sta- bility. Flawed risk …...

Lemmatization for the Sumerian language, compared to the modern languages, is much more challenging due to that it is a long dead language, highly skilled language experts are extremely scarce …...

Distantly supervised approaches have be- come popular in recent years as they allow training relation extractors without text- bound annotation, using instead known relations from a knowledge base and a …...

Historical newspapers are an important resource in humanities research, provid- ing the source materials about people and places in historical context. The Trove col- lection in the National Library of …...

Extracting named entities in text and link- ing extracted names to a given knowledge base are fundamental tasks in applications for text understanding. Existing systems typically run a named entity …...

We consider the task of named entity recognition for Chinese social media. The long line of work in Chinese NER has fo- cused on formal domains, and NER for social …...

We consider a novel setting for Named En- tity Recognition (NER) where we have ac- cess to document-specific knowledge base tags. These tags consist of a canonical name from a …...

We describe two tasks—named entity recog- nition (Task 1) and template slot filling (Task 2)—for clinical texts. The tasks leverage an- notations from the ShARe corpus, which con- sists of …...

This paper describes our participation in SemEval-2015 Task 12, and the opinion min- ing system sentiue. The general idea is that systems must determine the polarity of the sentiment expressed …...

Named entity recognition (NER) systems trained on newswire perform very badly when tested on Twitter. Signals that were reliable in copy-edited text disappear almost entirely in Twitter’s informal chatter, requiring …...

Our goal is to facilitate named en- tity recognition in Bulgarian texts by extending the coverage of DBpedia (http://www.dbpedia.org/) for Bul- garian. For this task we have trained translation Moses …...

Users combine attributes and types to describe and classify entities into cate- gories. These categories are fundamen- tal for organising knowledge in a decen- tralised way acting as tags and …...

AbstractIn recent years there has been a surge of interest in the natural language prosessing related to the real world, such as symbol grounding, language generation, and nonlinguistic data search …...

Word segmentation is usually recognized as the first step for many Chinese natural lan- guage processing tasks, yet its impact on these subsequent tasks is relatively under-studied. For example, how …...

AbstractThe availability of openly available textual datasets (“corpora”) with highly accurate manual annotations (“gold standard”) of named entities (e.g. persons, locations, organizations, etc.) is crucial in the training and evaluation …...

AbstractEarly detection and treatment of diseases that onset after a patient is admitted to a hospital, such as pneumonia, is critical to improving and reducing costs in healthcare. Previous studies …...

AbstractAnnotation projection is a practical method to deal with the low resource problem in incident languages (IL) processing. Previous methods on annotation projection mainly relied on word alignment results without …...

AbstractIn Sorani Kurdish, one of the most useful orthographic features in named-entity recognition – capitalization – is absent, as the language’s Perso-Arabic script does not make a distinction between uppercase …...

AbstractOne of the main obstacles, hampering method development and comparative evaluation of named entity recognition in social media, is the lack of a sizeable, diverse, high quality annotated corpus, analogous …...

AbstractWe describe and evaluate a character-level tagger for language-independent Named Entity Recognition (NER). Instead of words, a sentence is represented as a sequence of characters. The model consists of stacked …...

Context representations are central to vari- ous NLP tasks, such as word sense disam- biguation, named entity recognition, co- reference resolution, and many more. In this work we present a …...

Named Entity Recognition (NER) mod- els for language L are typically trained using annotated data in that language. We study cross-lingual NER, where a model for NER in L is …...

AbstractIn this paper we explain how we created a labelled corpus in English for a Named Entity Recognition (NER) task from multi-source and multi-domain data, for an industrial partner. We …...

In named entity recognition task especially for massive data like Twitter, having a large amount of high quality gazetteers can allevi- ate the problem of training data scarcity. One could …...

We introduce a novel technique called dynamic feature induction that keeps inducing high di- mensional features automatically until the fea- ture space becomes ‘more’ linearly separable. Dynamic feature induction searches …...

AbstractThis paper describes the named entity language resources developed as part of a development project for the South African languages. The development efforts focused on creating protocols and annotated data …...

The state-of-the-art named entity recognition (NER) systems are statistical machine learn- ing models that have strong generalization ca- pability (i.e., can recognize unseen entities that do not appear in training …...

AbstractMethods for Named Entity Recognition and Disambiguation (NERD) perform NER and NED in two separate stages. Therefore, NED may be penalized with respect to precision by NER false positives, and …...

AbstractIn this paper, we first build a manually annotated named entity corpus of Mongolian. Then, we propose three morphological processing methods and study comprehensive features, including syllable features, lexical features, …...

We describe an LSTM-based model which we call Byte-to-Span (BTS) that reads text as bytes and outputs span annotations of the form [start, length, label] where start posi- tions, lengths, …...

AbstractThis paper describes our construction of named-entity recognition (NER) systems in two Western Iranian languages, Sorani Kurdish and Tajik, as a part of a pilot study of “Linguistic Rapid Response” …...

AbstractRecognition of real-world entities is crucial for most NLP applications. Since its introduction some twenty years ago, named entity processing has undergone a significant evolution with, among others, the definition …...

State-of-the-art named entity recognition sys- tems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that are available. In this …...

AbstractNamed-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of …...

Named Entity Recognition is a well estab- lished information extraction task with many state of the art systems existing for a va- riety of languages. Most systems rely on language …...

AbstractThis work presents parallel corpora automatically annotated with several NLP tools, including lemma and part-of-speech tagging, named-entity recognition and classification, named-entity disambiguation, word-sense disambiguation, and coreference. The corpora comprise both …...

Most of the sequence tagging tasks in nat- ural language processing require to recog- nize segments with certain syntactic role or semantic meaning in a sentence. They are usually tackled …...

Public health surveillance is challenging due to difficulties accessing medical data in real-time. We present a novel, ef- fective and computationally inexpensive method for syndromic surveillance using Twitter data. The …...

AbstractIn this work we present the open source hunvec framework for sequential tagging, built upon Theano and Pylearn2. The underlying statistical model, which connects linear CRF-s with neural networks, was …...

AbstractIn this paper we investigate the usefulness of neural word embeddings in the process of translating Named Entities (NEs) from a resource-rich language to a language low on resources relevant …...

AbstractIn this paper, we study a novel approach for named entity recognition (NER) and mention detection (MD) in natural language processing. Instead of treating NER as a sequence labeling problem, …...

AbstractDespite sequences being core to NLP, scant work has considered how to handle noisy sequence labels from multiple annotators for the same text. Given such annotations, we consider two complementary …...

AbstractLexical resources such as dictionaries and gazetteers are often used as auxiliary data for tasks such as part-of-speech induction and named-entity recognition. However, discriminative training with lexical features requires annotated …...

AbstractNamed Entity Recognition (NER) is an important component of natural language processing (NLP), with applicability in biomedical domain, enabling knowledge-discovery from medical texts. Due to the fact that for the …...

We train a neural machine translation (NMT) system to both translate source- language text and copy target-language text, thereby exploiting monolingual cor- pora in the target language. Specifically, we create …...

AbstractWeighted finite automata and transducers (including hidden Markov models and conditional random fields) are widely used in natural language processing (NLP) to perform tasks such as morphological analysis, part-of-speech tagging, …...

AbstractComparing NLP systems to select the best one for a task of interest, such as named entity recognition, is critical for practitioners and researchers. A rigorous approach involves setting up …...

AbstractRich data provided by tweets have beenanalyzed, clustered, and explored in a variety of studies. Typically those studies focus on named entity recognition, entity linking, and entity disambiguation or clustering. …...

AbstractIn this paper, we utilize the linguistic structures of texts to improve named entity recognition by BRNN-CNN, a special bidirectional recursive network attached with a convolutional network. Motivated by the …...

AbstractThe purpose of this research is to address the problem of extracting information from travel itineraries and discuss the challenges faced in the process. Business-to-customer emails like booking confirmations and …...

AbstractNeural networks have achieved state-of-the-art performance on several structured-output prediction tasks, trained in a fully supervised fashion. However, annotated examples in structured domains are often costly to obtain, which thus …...

We investigate the problem of extracting mentions of medications and adverse drug events using sequence labelling and non- sequence labelling methods. We experi- ment with three different methods on two …...

AbstractThis paper describes our system, dubbed MoRS (Modular Ranking System), pronounced ‘Morse’, which participated in Task 3 of SemEval-2017. We used MoRS to perform the Community Question Answering Task 3, …...

AbstractWe propose a neural reranking system for named entity recognition (NER), leverages recurrent neural network models to learn sentence-level patterns that involve named entity mentions. In particular, given an output …...

AbstractBuilding knowledge bases (KB) automatically from text corpora is crucial for many applications such as question answering and web search. The problem is very challenging and has been divided into …...

AbstractNamed Entity Recognition (NER) poses new challenges in real-world documents in which there are entities with different roles according to their purpose or meaning. Retrieving all the possible entities in …...

AbstractThis paper addresses the problem of extracting keyphrases from scientific articles and categorizing them as corresponding to a task, process, or material. We cast the problem as sequence tagging and …...

AbstractPre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level …...

AbstractIn this paper we present the Tren-toTeam system which participated to thetask 3 at SemEval-2017 (Nakov et al.,2017).We concentrated our work onapplying Grice Maxims(used in manystate-of-the-art Machine learning applica-tions(Vogel et …...

AbstractNamed entities are frequently used in a metonymic manner. They serve as references to related entities such as people and organisations. Accurate identification and interpretation of metonymy can be directly …...

AbstractThe state-of-the-art named entity recognition (NER) systems are supervised machine learning models that require large amounts of manually annotated data to achieve high accuracy. However, annotating NER data by human …...

Monitoring mobility- and industry-relevant events is important in areas such as personal travel planning and supply chain management, but extracting events pertaining to specific companies, transit routes and locations from …...

AbstractNamed Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. NER systems have been studied and developed widely for decades, but …...

AbstractNamed entity recognition (NER) has attracted a substantial amount of research. Recently, several neural network-based models have been proposed and achieved high performance. However, there is little research on fine-grained …...

Adding external information from unlabeled data to Named Entity Recognition task is the direction of research for solving the scarcity of labeled data. In this issue, adding pre-trained context embeddings …...

Named-entity recognition (NER) can still be regarded as work in progress for a number of Asian languages due to the scarcity of annotated corpora. For this reason, with this paper …...

The lack of hand curated data is a major impediment to developing statistical semantic processors for many of the world languages. Our paper aims to bridge this gap by leveraging …...

AbstractGenerating character-level features is an important step for achieving good results in various natural language processing tasks. To alleviate the need for human labor in generating hand-crafted features, methods that …...

AbstractRecent advances in language modeling using recurrent neural networks have made it viable to model language as distributions over characters. By learning to predict the next character on the basis …...

AbstractWe investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include most of the state-of-the-art structures, and conduct …...

e-magyar is a new toolset for the analysis of Hungarian texts. It was produced as a collaborative effort of the Hungarian language technology community integrating the best state-of-the-art tools, enhancing …...

Recently, many studies indicate that charac- ter language model can capture syntactic- semantic word features, resulting in state-of- the-art performance in typical NLP sequence labeling tasks. This paper shows the …...

AbstractFundamental to several knowledge-centric applications is the need to identify named entities from their textual mentions. However, entities lack a unique representation and their mentions can differ greatly. These variations …...

Language Adaptation (similarly to Domain Adaptation) is a general approach to extend existing resources from a better resourced language (donor) to a lesser resourced one (recipient) by exploiting the lexical …...

AbstractNamed-entity Recognition (NER) is an important task in the NLP field , and is widely used to solve many challenges. However, in many scenarios, not all of the entities are …...

AbstractIn this paper, we propose to use a sequence to sequence model for Named Entity Recognition (NER) and we explore the effectiveness of such model in a progressive NER setting …...

In this paper, we describe a new system to extract, index, search, and visualize entities in Wikipedia. To carry out the entity extraction, we designed a high-performance, multilingual, entity linker …...

AbstractNeural network approaches to Named-Entity Recognition reduce the need for carefully hand-crafted features. While some features do remain in state-of-the-art systems, lexical features have been mostly discarded, with the exception …...

In dialogue systems, the tasks of named entity recognition (NER) and named entity linking (NEL) are vital preprocessing steps for understanding user intent, especially in open domain interaction where we …...

Recent approaches based on artificial neural networks (ANNs) have shown promising results for named-entity recognition (NER). In order to achieve high performances, ANNs need to be trained on a large …...

A major issue in machine translation (MT) applications is the recognition and translation of named entities. This is especially true for Chinese and Japanese, whose scripts present linguistic and algorithmic …...

Meaning Representation Parsing Charles Welch1, Jonathan K. Kummerfeld1, Song Feng2, Rada Mihalcea1 University of Michigan1, IBM Research2 {cfwelch, jkummerf, mihalcea}@umich.edu, sfeng@us.ibm.com Abstract In this paper we explore the role played …...

AbstractAttributing a particular property to a person by naming another person, who is typically wellknown for the respective property, is called a Vossian Antonomasia (VA). This subtpye of metonymy, which …...

AbstractWe study a variant of domain adaptation for named-entity recognition where multiple, heterogeneously tagged training sets are available. Furthermore, the test tag-set is not identical to any individual training tag-set. …...

AbstractMost state-of-the-art models for named entity recognition (NER) rely on the availability of large amounts of labeled data, making them challenging to extend to new, lower-resourced languages. However, there are …...

AbstractWe propose a morphologically informed model for named entity recognition, which is based on LSTM-CRF architecture and combines word embeddings, Bi-LSTM character embeddings, part-of-speech (POS) tags, and morphological information. While …...

The negative log-likelihood or cross entropy is the usual training objective of NLP mod- els owing to its versatility and empirical per- formance. However, training objectives which directly target the …...

AbstractNatural Language Processing has been perplexed for many years by the problem that multiple semantics are mixed inside a word, even with the help of context. To solve this problem, …...

AbstractSupervised approaches to named entity recognition (NER) are largely developed based on the assumption that the training data is fully annotated with named entity information. However, in practice, annotated data …...

AbstractNamed entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters. Therefore, Chinese Word Segmentation (CWS) is usually considered as the first step for …...

AbstractIn this work, we explore the way to perform named entity recognition (NER) using only unlabeled data and named entity dictionaries. To this end, we formulate the task as a …...

AbstractWe propose a new neural transfer method termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER). Specifically, two variants of DATNet, i.e., DATNet-F and DATNet-P, are …...

AbstractPrevious research shows that eye-tracking data contains information about the lexical and syntactic properties of text, which can be used to improve natural language processing models. In this work, we …...

AbstractWe examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction. Our framework (called DyGIE++) accomplishes all tasks by …...

AbstractGiven many recent advanced embedding models, selecting pre-trained word representation (i.e., word embedding) models best fit for a specific downstream NLP task is non-trivial. In this paper, we propose a …...

AbstractIn practical scenario, relation extraction needs to first identify entity pairs that have relation and then assign a correct relation class. However, the number of non-relation entity pairs in context …...

AbstractIn low-resource settings, the performance of supervised labeling models can be improved with automatically annotated or distantly supervised data, which is cheap to create but often noisy. Previous works have …...

AbstractCurrent region-based NER models only rely on fully-annotated training data to learn effective region encoder, which often face the training data bottleneck. To alleviate this problem, this paper proposes Gazetteer-Enhanced …...

AbstractIn countries that speak multiple main languages, mixing up different languages within a conversation is commonly called code-switching. Previous works addressing this challenge mainly focused on word-level aspects such as …...

AbstractIn this paper, we study differentiable neural architecture search (NAS) methods for natural language processing. In particular, we improve differentiable architecture search by removing the softmax-local constraint. Also, we apply …...

AbstractContextualized word representations are able to give different representations for the same word in different contexts, and they have been shown to be effective in downstream natural language processing tasks, …...

AbstractRecently, pre-trained language models have achieved remarkable success in a broad range of natural language processing tasks. However, in multilingual setting, it is extremely resource-consuming to pre-train a deep language …...

Named Entity Recognition (NER) is an essential task in Natural Language Processing. Memory Enhanced CRF (MECRF) integrates external memory to extend Conditional Random Field (CRF) to capture long-range dependencies with …...

AbstractNegative medical findings are prevalent in clinical reports, yet discriminating them from positive findings remains a challenging task for in-formation extraction. Most of the existing systems treat this task as …...

AbstractTraditional language models are unable to efficiently model entity names observed in text. All but the most popular named entities appear infrequently in text providing insufficient context. Recent efforts have …...

AbstractNamed entity recognition (NER) identifies typed entity mentions in raw text. While the task is well-established, there is no universally used tagset: often, datasets are annotated for use in downstream …...

AbstractHeuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary. Recent data-driven AL policy learning methods are also restricted to learn from closely …...

AbstractThe lack of word boundaries information has been seen as one of the main obstacles to develop a high performance Chinese named entity recognition (NER) system. Fortunately, the automatically constructed …...

AbstractIn cross-lingual transfer, NLP models over one or more source languages are applied to a low-resource target language. While most prior work has used a single source model or a …...

AbstractNamed entity recognition (NER) is one of the best studied tasks in natural language processing. However, most approaches are not capable of handling nested structures which are common in many …...

AbstractRecent developments in Named Entity Recognition (NER) have resulted in better and better models. However, is there a glass ceiling? Do we know which types of errors are still hard …...

AbstractIn this paper we discuss the named entity recognition task for Russian texts related to cybersecurity. First of all, we describe the problems that arise in course of labeling unstructured …...

AbstractSupervised machine learning assumes the availability of fully-labeled data, but in many cases, such as low-resource languages, the only data available is partially annotated. We study the problem of Named …...

AbstractFor those languages which use it, capitalization is an important signal for the fundamental NLP tasks of Named Entity Recognition (NER) and Part of Speech (POS) tagging. In fact, it …...

AbstractNamed entity recognition (NER) is widely used in natural language processing applications and downstream tasks. However, most NER tools target flat annotation from popular datasets, eschewing the semantic information available …...

AbstractShort texts challenge NLP tasks such as named entity recognition, disambiguation, linking and relation inference because they do not provide sufficient context or are partially malformed (e.g. wrt. capitalization, long …...

AbstractRecent work has validated the importance of subword information for word representation learning. Since subwords increase parameter sharing ability in neural models, their value should be even more pronounced in …...

AbstractWe introduce Rosita, a method to produce multilingual contextual word representations by training a single language model on text from multiple languages. Our method combines the advantages of contextual word …...

AbstractContextual string embeddings are a recent type of contextualized word embedding that were shown to yield state-of-the-art results when utilized in a range of sequence labeling tasks. They are based …...

AbstractPretrained contextual and non-contextual subword embeddings have become available in over 250 languages, allowing massively multilingual NLP. However, while there is no dearth of pretrained embeddings, the distinct lack of …...

AbstractThis paper compares how different machine learning classifiers can be used together with simple string matching and named entity recognition to detect locations in texts. We compare five different state-of-the-art …...

AbstractMost of the recently proposed neural models for named entity recognition have been purely data-driven, with a strong emphasis on getting rid of the efforts for collecting external resources or …...

AbstractWe present the Named Entity Recognition (NER) and disambiguation model used by the University of Arizona team (UArizona) for the SemEval 2019 task 12. We achieved fourth place on tasks …...

AbstractBuilding named entity recognition (NER) models for languages that do not have much training data is a challenging task. While recent work has shown promising results on cross-lingual transfer from …...

Named Entity Recognition (NER) is an essential task in Natural Language Processing. Memory Enhanced CRF (MECRF) integrates external memory to extend Conditional Random Field (CRF) to capture long-range dependencies with …...

AbstractWe present a new manually annotated corpus for broad-coverage named entity recognition for Finnish. Building on the original Universal Dependencies Finnish corpus of 754 documents (200,000 tokens) representing ten different …...

AbstractWe describe a dataset developed for Named Entity Recognition in German federal court decisions. It consists of approx. 67,000 sentences with over 2 million tokens. The resource contains 54,000 manually …...

AbstractFine-tuning pretrained model has achieved promising performance on standard NER benchmarks. Generally, these benchmarks are blessed with strong name regularity, high mention coverage and sufficient context diversity. Unfortunately, when scaling …...

AbstractThe task of named entity recognition (NER) is normally divided into nested NER and flat NER depending on whether named entities are nested or not. Models are usually separately developed …...

AbstractThe recent success of pretrained language models in Natural Language Processing has sparked interest in training such models for languages other than English. Currently, training of these models can either …...

AbstractThis paper tackles the task of named entity recognition (NER) applied to digitized historical texts obtained from processing digital images of newspapers using optical character recognition (OCR) techniques. We argue …...

AbstractSimple yet effective data augmentation techniques have been proposed for sentence-level and sentence-pair natural language processing tasks. Inspired by these efforts, we design and compare data augmentation for named entity …...

AbstractUnlike widely used Named Entity Recognition (NER) data sets in generic domains, biomedical NER data sets often contain mentions consisting of discontinuous spans. Conventional sequence tagging techniques encode Markov assumptions …...

AbstractMultilingual pre-trained Transformers, such as mBERT (Devlin et al., 2019) and XLM-RoBERTa (Conneau et al., 2020a), have been shown to enable effective cross-lingual zero-shot transfer. However, their performance on Arabic …...

AbstractWe propose a generative framework for joint sequence labeling and sentence-level classification. Our model performs multiple sequence labeling tasks at once using a single, shared natural language output space. Unlike …...

AbstractThere has been an influx of biomedical domain-specific language models, showing language models pre-trained on biomedical text perform better on biomedical domain benchmarks than those trained on general domain text …...

AbstractNamed entities pose a unique challenge to traditional methods of language modeling. While several domains are characterised with a high proportion of named entities, the occurrence of specific entities varies …...

AbstractNamed entity recognition (NER) identifies spans of text that contain names. Many researchers have reported the results of NER on text created through optical character recognition (OCR) over the past …...

AbstractWe address the problem of calibrating prediction confidence for output entities of interest in natural language processing (NLP) applications. It is important that NLP applications such as named entity recognition …...

AbstractWe present CAMeL Tools, a collection of open-source tools for Arabic natural language processing in Python. CAMeL Tools currently provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition …...

AbstractNamed entity recognition (NER) aims to identify text spans that mention named entities and classify them into pre-defined categories. For Chinese NER task, most of the existing methods are character-based …...

AbstractAs an essential task in task-oriented dialog systems, slot filling requires extensive training data in a certain domain. However, such data are not always available. Hence, cross-domain slot filling has …...

AbstractThere is an increasing interest in studying natural language and computer code together, as large corpora of programming texts become readily available on the Internet. For example, StackOverflow currently has …...

AbstractCurrent state-of-the-art models for named entity recognition (NER) are neural models with a conditional random field (CRF) as the final layer. Entities are represented as per-token labels with a special …...

AbstractPast progress on neural models has proven that named entity recognition is no longer a problem if we have enough labeled data. However, collecting enough data and annotating them are …...

AbstractOut-of-vocabulary words are still a challenge in cross-lingual Natural Language Processing tasks, for which transliteration from source to target language or script is one of the solutions. In this study, …...

AbstractAs the number of digitized archival documents increases very rapidly, named entity recognition (NER) in historical documents has become very important for information extraction and data mining. For this task …...

AbstractThis paper introduces DAN+, a new multi-domain corpus and annotation guidelines for Dan-ish nested named entities (NEs) and lexical normalization to support research on cross-lingualcross-domain learning for a less-resourced language. …...

AbstractSpan identification (in short, span ID) tasks such as chunking, NER, or code-switching detection, ask models to identify and classify relevant spans in a text. Despite being a staple of …...

AbstractRecent work has shown the importance of adaptation of broad-coverage contextualised embedding models on the domain of the target task of interest. Current self-supervised adaptation methods are simplistic, as the …...

AbstractNamed Entity Recognition (NER) is an essential component of many Natural Language Processing pipelines. However, building these language dependent models requires large amounts of annotated data. Crowdsourcing emerged as a …...

AbstractThe French TreeBank developed at the University Paris 7 is the main source of morphosyntactic and syntactic annotations for French. However, it does not include explicit information related to named …...

AbstractWe set out to uncover the unique grammatical properties of an important yet so far under-researched type of natural language text: that of short labels typically found within structured datasets. …...

AbstractMultilingual BERT (M-BERT) has been a huge success in both supervised and zero-shot cross-lingual transfer learning. However, this success is focused only on the top 104 languages in Wikipedia it …...

AbstractMassively multilingual transformers (MMTs) pretrained via language modeling (e.g., mBERT, XLM-R) have become a default paradigm for zero-shot language transfer in NLP, offering unmatched transfer performance. Current evaluations, however, verify …...

AbstractIn this work we present the experiments which lead to the creation of our BERT and ELECTRA based German language models, GBERT and GELECTRA. By varying the input training data, …...

AbstractCode-switching is the use of more than one language in the same conversation or utterance. Recently, multilingual contextual embedding models, trained on multiple monolingual corpora, have shown promising results on …...

AbstractThis paper describes our approach to “DeftEval: Extracting Definitions from Free Text in Textbooks” competition held as a part of Semeval 2020. The task was devoted to finding and labeling …...

AbstractTo assess the robustness of NER systems, we propose an evaluation method that focuses on subsets of tokens that represent specific sources of errors: unknown words and label shift or …...

AbstractThe CoNLL-2003 corpus for English-language named entity recognition (NER) is one of the most influential corpora for NER model research. A large number of publications, including many landmark works, have …...

AbstractMotion recognition is one of the basic cognitive capabilities of many life forms, however, detecting and understanding motion in text is not a trivial task. In addition, identifying motion entities …...

AbstractNamed entity recognition (NER) is highly sensitive to sentential syntactic and semantic properties where entities may be extracted according to how they are used and placed in the running text. …...

AbstractThe correct translation of named entities (NEs) still poses a challenge for conventional neural machine translation (NMT) systems. This study explores methods incorporating named entity recognition (NER) into NMT with …...

AbstractIn this paper, we introduce NLP resources for 11 major Indian languages from two major language families. These resources include: (a) large-scale sentence-level monolingual corpora, (b) pre-trained word embeddings, (c) …...

AbstractInterpretable rationales for model predictions play a critical role in practical applications. In this study, we develop models possessing interpretable inference process for structured prediction. Specifically, we present a method …...

AbstractWith the proliferation of models for natural language processing tasks, it is even harder to understand the differences between models and their relative merits. Simply looking at differences between holistic …...

AbstractIn recent years, the plentiful information contained in Chinese legal documents has attracted a great deal of attention because of the large-scale release of the judgment documents on China Judgments …...

AbstractHTML tags are typically discarded in free text Named Entity Recognition from Web pages. We investigate whether these discarded tags might be used to improve NER performance. We compare Text+Tags …...

AbstractRecent trends in NLP research have raised an interest in linguistic code-switching (CS); modern approaches have been proposed to solve a wide range of NLP tasks on multiple language pairs. …...

AbstractNamed Entity Recognition (NER) is one of the first stages in deep language understanding yet current NER models heavily rely on human-annotated data. In this work, to alleviate the dependence …...

AbstractThe main goal behind state-of-the-art pre-trained multilingual models such as multilingual BERT and XLM-R is enabling and bootstrapping NLP applications in low-resource languages through zero-shot or few-shot cross-lingual transfer. However, …...

AbstractThe success of several architectures to learn semantic representations from unannotated text and the availability of these kind of texts in online multilingual resources such as Wikipedia has facilitated the …...

AbstractThe MICE project (2018-2020) will deliver a middleware layer for improving the output quality of the eTranslation system of EC’s Connecting Europe Facility through additional services, such as domain adaptation …...

AbstractNamed entity recognition is a key component of many text processing pipelines and it is thus essential for this component to be robust to different types of input. However, domain …...

AbstractNamed Entity Recognition (NER) is a fundamental task in Natural Language Processing, concerned with identifying spans of text expressing references to entities. NER research is often focused on flat entities …...

AbstractExisting approaches for named entity recognition suffer from data sparsity problems when conducted on short and informal texts, especially user-generated social media content. Semantic augmentation is a potential way to …...

AbstractNamed entity recognition is a critical task in the natural language processing field. Most existing methods for this task can only exploit contextual information within a sentence. However, their performance …...

AbstractDeep neural network models have helped named entity recognition achieve amazing performance without handcrafting features. However, existing systems require large amounts of human annotated training data. Efforts have been made …...

AbstractNamed entity recognition (NER) is an important task in the natural language processing field. Existing NER methods heavily rely on labeled data for model training, and their performance on rare …...

AbstractNamed Entity Recognition (NER) performance often degrades rapidly when applied to target domains that differ from the texts observed during training. When in-domain labelled data is available, transfer learning techniques …...

AbstractSequence labeling systems should perform reliably not only under ideal conditions but also with corrupted inputs—as these systems often process user-generated text or follow an error-prone upstream component. To this …...

AbstractNamed entity recognition is a key component in various natural language processing systems, and neural architectures provide significant improvements over conventional approaches. Regardless of different word embedding and hidden layer …...

AbstractIncorporating lexicons into character-level Chinese NER by lattices is proven effective to exploitrich word boundary information. Previous work has extended RNNs to consume lattice inputsand achieved great success. However, due …...

AbstractThe paper reports on the usage of deep learning methods for improving a Named Entity Recognition (NER) training corpus and for predicting and annotating new types in a test corpus. …...

AbstractNamed entity recognition identifies common classes of entities in text, but these entity labels are generally sparse, limiting utility to downstream tasks. In this work we present ScienceExamCER, a densely-labeled …...

AbstractRisk management is a vital activity to ensure employee safety in construction projects. Various documents provide important supporting evidence, including details of previous incidents, consequences and mitigation strategies. Potential hazards …...

AbstractVirtual adversarial training (VAT) is a powerful technique to improve model robustness in both supervised and semi-supervised settings. It is effective and can be easily adopted on lots of image …...

AbstractTo better tackle the named entity recognition (NER) problem on languages with little/no labeled data, cross-lingual NER must effectively leverage knowledge learned from source languages with rich labeled data. Previous …...

AbstractTraditional named entity recognition models use gazetteers (lists of entities) as features to improve performance. Although modern neural network models do not require such hand-crafted features for strong performance, recent …...

AbstractFor over thirty years researchers have studied the problem of automatically detecting named entities in written language. Throughout this time the majority of such work has focused on detection and …...

AbstractNatural language processing models often have to make predictions on text data that evolves over time as a result of changes in language use or the information described in the …...

AbstractThis paper presents a new challenging information extraction task in the domain of materials science. We develop an annotation scheme for marking information on experiments related to solid oxide fuel …...

AbstractIn this work, we explore the way to quickly adjust an existing named entity recognition (NER) system to make it capable of recognizing entity types not defined in the system. …...

AbstractApplying natural language processing (NLP) to medical and clinical texts can bring important social benefits by mining valuable information from unstructured text. A popular application for that purpose is named …...

AbstractMost recent improvements in NLP come from changes to the neural network architectures modeling the text input. Yet, state-of-the-art models often rely on simple approaches to model the label space, …...

AbstractTraining neural models for named entity recognition (NER) in a new domain often requires additional human annotations (e.g., tens of thousands of labeled instances) that are usually expensive and time-consuming …...

AbstractNamed entity recognition and relation extraction are two important fundamental problems. Joint learning algorithms have been proposed to solve both tasks simultaneously, and many of them cast the joint task …...

AbstractBuilding predictive models for information extraction from text, such as named entity recognition or the extraction of semantic relationships between named entities in text, requires a large corpus of annotated …...

AbstractNamed entity recognition (NER) from speech is usually made through a pipeline process that consists in (i) processing audio using an automatic speech recognition system (ASR) and (ii) applying a …...

AbstractLanguage Models have long been a prolific area of study in the field of Natural Language Processing (NLP). One of the newer kinds of language models, and some of the …...

AbstractResearch on overlapped and discontinuous named entity recognition (NER) has received increasing attention. The majority of previous work focuses on either overlapped or discontinuous entities. In this paper, we propose …...

AbstractThis paper tackles a new task for event entity recognition (EER). Different from named entity recognizing (NER) task it only identifies the named entities which are related to a specific …...

AbstractNamed Entity Recognition (NER) is the task of identifying spans that represent entities in sentences. Whether the entity spans are nested or discontinuous, the NER task can be categorized into …...

AbstractNeural methods have been shown to achieve high performance in Named Entity Recognition (NER), but rely on costly high-quality labeled data for training, which is not always available across languages. …...

Multilingual language models have been a crucial breakthrough as they considerably re- duce the need of data for under-resourced languages. Nevertheless, the superiority of language-specific models has already been proven …...

AbstractNamed entity recognition (NER) is one of the major tasks in natural language processing. A named entity is often a word or expression that bears a valuable piece of information, …...

AbstractIt has been shown that named entity recognition (NER) could benefit from incorporating the long-distance structured information captured by dependency trees. We believe this is because both types of features …...

AbstractRecent multilingual pre-trained language models have achieved remarkable zero-shot performance, where the model is only finetuned on one source language and directly evaluated on target languages. In this work, we …...

AbstractThe current natural language processing is strongly focused on raising accuracy. The progress comes at a cost of super-heavy models with hundreds of millions or even billions of parameters. However, …...

AbstractIn this work, we examine the ability of NER models to use contextual information when predicting the type of an ambiguous entity. We introduce NRB, a new testbed carefully designed …...

AbstractCode-Mixing (CM) is a common phenomenon in multilingual societies. CM plays a significant role in technology and medical fields where terminologies in the native language are not available or known. …...

AbstractThe current COVID-19 pandemic has lead to the creation of many corpora that facilitate NLP research and downstream applications to help fight the pandemic. However, most of these corpora are …...

Named Entity Recognition (NER) is a key Natural Language Processing task. However, most existing work on NER targets flat named entities (NEs) and ignores the recognition of nested structures, where …...

AbstractCrowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers. Previous studies focus on reducing the influences from the …...

AbstractCurrent work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models. However, most existing techniques focus on augmenting in-domain data in low-resource scenarios where …...

AbstractDistant supervision tackles the data bottleneck in NER by automatically generating training instances via dictionary matching. Unfortunately, the learning of DS-NER is severely dictionary-biased, which suffers from spurious correlations and …...

Large-scale conversational assistants like Alexa, Siri, Cortana and Google Assistant process every utterance using multiple models for domain, intent and named entity recognition. Given the decoupled nature of model development …...

AbstractNamed entity recognition (NER) remains challenging when entity mentions can be discontinuous. Existing methods break the recognition process into several sequential steps. In training, they predict conditioned on the golden …...

AbstractWe study the problem of training named entity recognition (NER) models using only distantly-labeled data, which can be automatically obtained by matching entity mentions in the raw text with entity …...

AbstractMuch recent work suggests that incorporating syntax information from dependency trees can improve task-specific transformer models. However, the effect of incorporating dependency tree information into pre-trained transformer models (e.g., BERT) …...

AbstractTextual information extraction is a typical research topic in the NLP community. Several NLP tasks such as named entity recognition and relation extraction between entities have been well-studied in previous …...

AbstractZero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English. While the advance of …...

AbstractRecently, considerable literature has grown up around the theme of few-shot named entity recognition (NER), but little published benchmark data specifically focused on the practical and challenging task. Current approaches …...

AbstractThis paper presents an empirical study to efficiently build named entity recognition (NER) systems when a small amount of in-domain labeled data is available. Based upon recent Transformer-based self-supervised pre-trained …...

AbstractInstead of using expensive manual annotations, researchers have proposed to train named entity recognition (NER) systems using heuristic labeling rules. However, devising labeling rules is challenging because it often requires …...

AbstractFor manufacturers of home appliances, the Studying discussion of products on social media can help manufacturers improve their products. Opinions provided through online reviews can immediately reflect whether the product …...

AbstractPretrained multilingual models (PMMs) enable zero-shot learning via cross-lingual transfer, performing best for languages seen during pretraining. While methods exist to improve performance for unseen languages, they have almost exclusively …...

AbstractIdentifying named entities in written text is an essential component of the text processing pipeline used in applications such as text editors to gain a better understanding of the semantics …...

AbstractThough language model text embeddings have revolutionized NLP research, their ability to capture high-level semantic information, such as relations between entities in text, is limited. In this paper, we propose …...

AbstractDeep neural networks have achieved state-of-the-art performances on named entity recognition(NER) with sufficient training data while they perform poorly in low-resource scenarios due to data scarcity. To solve this problem …...

AbstractPretraining-based neural network models have demonstrated state-of-the-art (SOTA) performances on natural language processing (NLP) tasks. The most frequently used sentence representation for neural-based NLP methods is a sequence of subwords …...

We consider a joint information extraction (IE) model, solving named entity recognition, coreference resolution and relation extraction jointly over the whole document. In particu- lar, we study how to inject …...

AbstractFew-shot Named Entity Recognition (NER) exploits only a handful of annotations to iden- tify and classify named entity mentions. Pro- totypical network shows superior performance on few-shot NER. However, existing …...

AbstractLexicon information and pre-trained models, such as BERT, have been combined to explore Chinese sequence labeling tasks due to their respective strengths. However, existing methods solely fuse lexicon features via …...

AbstractNamed entity recognition (NER) is a well-studied task in natural language processing. Traditional NER research only deals with flat entities and ignores nested entities. The span-based methods treat entity recognition …...

AbstractWe take a step towards addressing the under- representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset …...

AbstractPublic datasets are often used to evaluate the efficacy and generalizability of state-of-the-art methods for many tasks in natural language processing (NLP). However, the presence of overlap between the train …...

AbstractThe combination of multilingual pre-trained representations and cross-lingual transfer learning is one of the most effective methods for building functional NLP systems for low-resource languages. However, for extremely low-resource languages …...

AbstractAlthough the existing Named Entity Recognition (NER) models have achieved promising performance, they suffer from certain drawbacks. The sequence labeling-based NER models do not perform well in recognizing long entities …...

AbstractMultilingual T5 pretrains a sequence-to-sequence model on massive monolingual texts, which has shown promising results on many cross-lingual tasks. In this paper, we improve multilingual text-to-text transfer Transformer with translation …...

AbstractNamed Entity Recognition (NER) for low-resource languages is a both practical and challenging research problem. This paper addresses zero-shot transfer for cross-lingual NER, especially when the amount of source-language training …...

AbstractAlthough pre-trained big models (e.g., BERT, ERNIE, XLNet, GPT3 etc.) have delivered top performance in Seq2seq modeling, their deployments in real-world applications are often hindered by the excessive computations and …...

AbstractThere is an incredible amount of information available in the form of textual documents due to the growth of information sources. In order to get the information into an actionable …...

Weakly supervised methods estimate the la- bels for a dataset using the predictions of sev- eral noisy supervision sources. Many machine learning practitioners have begun using weak supervision to more …...

Named entity recognition (NER) is a funda- mental task in natural language processing, these is a long held belief that datasets benefit the model. However, not all the data help …...

AbstractWeak supervision has shown promising results in many natural language processing tasks, such as Named Entity Recognition (NER). Existing work mainly focuses on learning deep NER models only with weak …...

AbstractIn this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction. NEREL is significantly larger than existing Russian datasets: to date it contains 56K annotated …...

Incorporating external knowledge into Named Entity Recognition (NER) systems has been widely studied in the generic domain. In this paper, we focus on clinical domain where only limited data is …...

AbstractImages are core components of multi-modal learning in natural language processing (NLP), and results have varied substantially as to whether images improve NLP tasks or not. One confounding effect has …...

AbstractMost combinations of NLP tasks and language varieties lack in-domain examples for supervised training because of the paucity of annotated data. How can neural models make sample-efficient generalizations from task–language …...

AbstractWe study learning named entity recognizers in the presence of missing entity annotations. We approach this setting as tagging with latent variables and propose a novel loss, the Expected Entity …...

AbstractRecent years have seen the paradigm shift of Named Entity Recognition (NER) systems from sequence labeling to span prediction. Despite its preliminary effectiveness, the span prediction model’s architectural bias has …...

AbstractActive Learning (AL) has been successfully applied to Deep Learning in order to drastically reduce the amount of data required to achieve high performance. Previous works have shown that lightweight …...

AbstractIn recent years, we have seen a colossal effort in pre-training multilingual text encoders using large-scale corpora in many languages to facilitate cross-lingual transfer learning. However, due to typological differences …...

AbstractTo alleviate label scarcity in Named Entity Recognition (NER) task, distantly supervised NER methods are widely applied to automatically label data and identify entities. Although the human effort is reduced, …...

AbstractRecognizing named entities in short search engine queries is a difficult task due to their weaker contextual information compared to long sentences. Standard named entity recognition (NER) systems that are …...

AbstractNowadays, named entity recognition (NER) achieved excellent results on the standard corpora. However, big issues are emerging with a need for an application in a specific domain, because it requires …...

AbstractThe increment of toxic comments on online space is causing tremendous effects on other vulnerable users. For this reason, considerable efforts are made to deal with this, and SemEval-2021 Task …...

AbstractContextual word embedding models, such as BioBERT and Bio_ClinicalBERT, have achieved state-of-the-art results in biomedical natural language processing tasks by focusing their pre-training process on domain-specific corpora. However, such models …...

AbstractUnsupervised consistency training is a way of semi-supervised learning that encourages consistency in model predictions between the original and augmented data. For Named Entity Recognition (NER), existing approaches augment the …...

AbstractData sharing restrictions are common in NLP datasets. The purpose of this task is to develop a model trained in a source domain to make predictions for a target domain …...

AbstractThis paper describes the research developed at Unbabel, a Portuguese Machine-translation start-up, that combines MT with human post-edition and focuses strictly on customer service content. We aim to contribute to …...

AbstractNamed entity recognition has become an increasingly useful tool for digital humanities research, specially when it comes to historical texts. However, historical texts pose a wide range of challenges to …...

AbstractWe present a biomedical knowledge enhanced pre-trained language model for medicinal product vertical search. Following ELECTRA’s replaced token detection (RTD) pre-training, we leverage biomedical entity masking (EM) strategy to learn …...

Abstract“Named entity recognition and relation extraction are core sub-tasks of relational triple extraction. Recent studies have used parameter sharing or joint decoding to create interaction between these two tasks. However, …...

AbstractThis paper presents the system description of team AaltoNLP for SemEval-2022 shared task 11: MultiCoNER. Transformer-based models have produced high scores on standard Named Entity Recognition (NER) tasks. However, accuracy …...

AbstractRecently, Transformer has achieved great success in Chinese named entity recognition (NER) owing to its good parallelism and ability to model long-range dependencies, which utilizes self-attention to encode context. However, …...

AbstractMultilingual pretrained models, while effective on monolingual data, need additional training to work well with code-switched text. In this work, we present a novel idea of training multilingual models with …...

In this paper, we present a pipeline for auto- matic minuting. This pipeline is an end-to-end system for minuting the multiparty dialogues of meetings. It provides multilingual com- munication and …...

AbstractFew-Shot Sequence Labeling (FSSL) is a canonical paradigm for the tagging models, e.g., named entity recognition and slot filling, to generalize on an emerging, resource-scarce domain. Recently, the metric-based meta-learning …...

A vehicle recall system is a process of recall- ing and repairing vehicles with defective de- signs or potential for accidents and failures. The recall document concisely explains the circumstances …...

AbstractSocial media data such as Twitter messages (“tweets”) pose a particular challenge to NLP systems because of their short, noisy, and colloquial nature. Tasks such as Named Entity Recognition (NER) …...

AbstractCurrent practices in building new NLP models for low-resourced languages rely either on Machine Translation of training sets from better resourced languages or on cross-lingual transfer from them. Still we …...

AbstractThis paper introduces our Diversity Advanced Actor-Critic reinforcement learning (A2C) framework (DAAC) to improve the generalization and accuracy of Natural Language Processing (NLP). We show that the diversification of training …...

AbstractArtificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually offering great promise for medical practice. With the development of biomedical language understanding benchmarks, AI applications …...

AbstractNatural language understanding (NLU) tasks are typically defined by creating an annotated dataset in which each utterance is encountered once. Such data does not resemble real-world natural language interactions in …...

AbstractInterpolative data augmentation has proven to be effective for NLP tasks. Despite its merits, the sample selection process in mixup is random, which might make it difficult for the model …...

AbstractIdentifying named entities is, in general, a practical and challenging task in the field of Natural Language Processing. Named Entity Recognition on the code-mixed text is further challenging due to …...

AbstractThe growing interest in named entity recognition (NER) in various domains has led to the creation of different benchmark datasets, often with slightly different annotation guidelines. To better understand the …...

AbstractNamed Entity Recognition (NER) in Few-Shot setting is imperative for entity tagging in low resource domains. Existing approaches only learn class-specific semantic features and intermediate representations from source domains. This …...

AbstractThis study uses training and validation data from the “ROCLING 2022 Chinese Health Care Named Entity Recognition Task” for modeling. The modeling process adopts technologies such as data augmentation and …...

AbstractThe MultiCoNER shared task aims at detecting semantically ambiguous and complex named entities in short and low-context settings for multiple languages. The lack of contexts makes the recognition of ambiguous …...

AbstractNamed entity recognition (NER) is a fundamental task to recognize specific types of entities from a given sentence. Depending on how the entities appear in the sentence, it can be …...

AbstractIn this paper we present the Serbian part of the ELTeC multilingual corpus of novels written in the time period 1840-1920. The corpus is being built in order to test …...

AbstractIn this paper, we study the named entity recognition (NER) problem under distant supervision. Due to the incompleteness of the external dictionaries and/or knowledge bases, such distantly annotated training data …...

AbstractRunning large-scale pre-trained language models in computationally constrained environments remains a challenging problem yet to be addressed, while transfer learning from these models has become prevalent in Natural Language Processing …...

AbstractSuccessful Machine Learning based Named Entity Recognition models could fail on texts from some special domains, for instance, Chinese addresses and e-commerce titles, where requires adequate background knowledge. Such texts …...

AbstractNamed entity recognition (NER) in a real-world setting remains challenging and is impacted by factors like text genre, corpus quality, and data availability. NER models trained on CoNLL do not …...

AbstractSpeaker identification (SI) in texts aims to identify the speaker(s) for each utterance in texts. Previous studies divide SI into several sub-tasks (e.g., quote extraction, named entity recognition, gender identification, …...

AbstractModern approaches in Natural Language Processing (NLP) require, ideally, large amounts of labelled data for model training. However, new language resources, for example, for Named Entity Recognition (NER), Co-reference Resolution …...

AbstractThe introduction of word embedding models has remarkably changed many Natural Language Processing tasks. Word embeddings can automatically capture the semantics of words and other hidden features. Nonetheless, the Arabic …...

AbstractTransformer-based language models trained on large natural language corpora have been very useful in downstream entity extraction tasks. However, they often result in poor performances when applied to domains that …...

AbstractThis paper proposes to cast end-to-end span-based SRL as a word-based graph parsing task. The major challenge is how to represent spans at the word level. Borrowing ideas from research …...

AbstractPrevious work of class-incremental learning for Named Entity Recognition (NER) relies on the assumption that there exists abundance of labeled data for the training of new classes. In this work, …...

AbstractFew-shot named entity recognition (NER) enables us to build a NER system for a new domain using very few labeled examples. However, existing prototypical networks for this task suffer from …...

AbstractDespite remarkable advances in the development of language resources over the recent years, there is still a shortage of annotated, publicly available corpora covering (German) medical language. With the initial …...

AbstractRecent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates. Similar attempts have been made on named entity recognition (NER) which manually design …...

AbstractNamed entity recognition (NER) is a fundamental and important task in NLP, aiming at identifying named entities (NEs) from free text. Recently, since the multi-head attention mechanism applied in the …...

AbstractNamed Entity Recognition (NER) is a foundational NLP task that aims to provide class labels like Person, Location, Organisation, Time, and Number to words in free text. Named Entities can …...

AbstractMultilingual models are often particularly dependent on scaling to generalize to a growing number of languages. Compression techniques are widely relied upon to reconcile the growth in model size with …...

Japanese named entities extracted from auto- matic speech recognition frequently contain speech recognition errors and unknown named entities due to abbreviations and aliases. One possible solution to this problem of …...

AbstractIn this paper we present KIND, an Italian dataset for Named-entity recognition. It contains more than one million tokens with annotation covering three classes: person, location, and organization. The dataset …...

AbstractPre-trained language models such as BERT have been successful at tackling many natural language processing tasks. However, the unsupervised sub-word tokenization methods commonly used in these models (e.g., byte-pair encoding …...

AbstractAs digitized traditional cultural heritage documents have rapidly increased, resulting in an increased need for preservation and management, practical recognition of entities and typification of their classes has become essential. …...

AbstractWe present LANTERN, a multi-stage transformer architecture for named-entity recognition (NER) designed to operate on indefinitely large text sequences (i.e. > 512 elements). For a given image of a form …...

AbstractTraditional methods for named entity recognition (NER) classify mentions into a fixed set of pre-defined entity types. However, in many real-world scenarios, new entity types are incrementally involved. To investigate …...

AbstractNamed Entity Recognition (NER) systems often demonstrate great performance on in-distribution data, but perform poorly on examples drawn from a shifted distribution. One way to evaluate the generalization ability of …...

AbstractMultilingual pre-trained models are known to suffer from the curse of multilinguality, which causes per-language performance to drop as they cover more languages. We address this issue by introducing language-specific …...

AbstractMost NER methods rely on extensive labeled data for model training, which struggles in the low-resource scenarios with limited training data. Existing dominant approaches usually suffer from the challenge that …...

Abstract“This paper introduces DepTrigger, a simple and effective model in low-resource named entity recognition (NER) based on multi-hop dependency triggers. Dependency triggers refer to salient nodes relative to an entity …...

AbstractAfrican languages are spoken by over a billion people, but they are under-represented in NLP research and development. Multiple challenges exist, including the limited availability of annotated training and evaluation …...

AbstractState-of-the-art pre-trained language models have been shown to memorise facts and perform well with limited amounts of training data. To gain a better understanding of how these models learn, we …...

AbstractNamed Entity Recognition (NER) tools have been in development for years, yet few have been aimed at medical documents. The increasing needs for analyzing medical data makes it crucial to …...

AbstractWe present AnonData, a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing …...

AbstractThis study aims to evaluate three most popular word segmentation tool for a large Traditional Chinese corpus in terms of their efficiency, resource consumption, and cost. Specifically, we compare the …...

AbstractThis paper presents a new historical language resource, a corpus of Estonian Parish Court records from the years 1821-1920, annotated for named entities (NE), and reports on named entity recognition …...

AbstractRecent progress in language model pre-training has led to important improvements in Named Entity Recognition (NER). Nonetheless, this progress has been mainly tested in well-formatted documents such as news, Wikipedia, …...

AbstractIdioms are lexically-complex phrases whose meaning cannot be derived by compositionally interpreting their components. Although the automatic identification and understanding of idioms is essential for a wide range of Natural …...

AbstractSeveral popular Transformer based language models have been found to be successful for text-driven brain encoding. However, existing literature leverages only pretrained text Transformer models and has not explored the …...

AbstractSpoken language understanding (SLU) tasks involve mapping from speech signals to semantic labels. Given the complexity of such tasks, good performance is expected to require large labeled datasets, which are …...

AbstractThis paper describes the ROCLING-2022 shared task for Chinese healthcare named entity recognition, including task description, data preparation, performance metrics, and evaluation results. Among ten registered teams, seven participating teams …...

AbstractNamed entity recognition (NER) is a fundamental task in natural language processing. Recent works treat named entity recognition as a reading comprehension task, constructing type-specific queries manually to extract entities. …...

AbstractAchieving good performance on few-shot or zero-shot datasets has been a long-term challenge for NER. The conventional semantic transfer approaches on NER will decrease model performance when the semantic distribution …...

AbstractMulti-modal techniques offer significant untapped potential to unlock improved NLP technology for local languages. However, many advances in language model pre-training are focused on text, a fact that only increases …...

AbstractThis paper aims for a potential architectural improvement for multilingual learning and asks: Can different tasks from different languages be modeled in a monolithic framework, i.e. without any task/language-specific module? …...

AbstractIn this paper, we evaluate several Transformer-based language models for Icelandic on four downstream tasks: Part-of-Speech tagging, Named Entity Recognition. Dependency Parsing, and Automatic Text Summarization. We pre-train four types …...

AbstractMultiple works have proposed to probe language models (LMs) for generalization in named entity (NE) typing (NET) and recognition (NER). However, little has been done in this direction for auto-regressive …...

AbstractNamed Entity Recognition (NER), an essential subtask in NLP that identifies text belonging to predefined semantics such as a person, location, organization, drug, time, clinical procedure, biological protein, etc. NER …...

AbstractText mining and information extraction for the medical domain has focused on scientific text generated by researchers. However, their access to individual patient experiences or patient-doctor interactions is limited. On …...

AbstractNegative sampling is highly effective in handling missing annotations for named entity recognition (NER). One of our contributions is an analysis on how it makes sense through introducing two insightful …...

AbstractIn this study, a named entity recognition was constructed and applied to the identification of Chinese medicine names and disease names. The results can be further used in a human-machine …...

AbstractSelf-augmentation has received increasing research interest recently to improve named entity recognition (NER) performance in low-resource scenarios. Token substitution and mixup are two feasible heterogeneous self-augmentation techniques for NER that …...

AbstractQuestion answering (QA) is one of the most common NLP tasks that relates to named entity recognition, fact extraction, semantic search and some other fields. In industry, it is much …...

AbstractUnlabeled Entity Problem (UEP) in Named Entity Recognition (NER) datasets seriously hinders the improvement of NER performance. This paper proposes SCL-RAI to cope with this problem. Firstly, we decrease the …...

AbstractIn this study, named entity recognition is constructed and applied in the medical domain. Data is labeled in BIO format. For example, “muscle” would be labeled “B-BODY” and “I-BODY”, and …...

AbstractNamed entity recognition generally refers to entities with specific meanings in unstructured text, including names of people, places, organizations, dates, times, quantities, proper nouns and other words. In the medical …...

AbstractLarge scale pre-training models have been widely used in named entity recognition (NER) tasks. However, model ensemble through parameter averaging or voting can not give full play to the differentiation …...

AbstractWe present Transformer based pretrained models, which are fine-tuned for Named Entity Recognition (NER) task. Our team participated in SemEval-2022 Task 11 MultiCoNER: Multilingual Complex Named Entity Recognition task for …...

AbstractNamed Entity Recognition (NER) is an important task in Natural Language Processing that aims to identify text spans belonging to predefined categories. Traditional NER systems ignore nested entities, which are …...

AbstractLarge multilingual language models generally demonstrate impressive results in zero-shot cross-lingual transfer, yet often fail to successfully transfer to low-resource languages, even for token-level prediction tasks like named entity recognition …...

AbstractCan NLP assist in building formal models for verifying complex systems? We study this challenge in the context of parsing Network File System (NFS) specifications. We define a semantic-dependency problem …...

AbstractIn this work, we take the named entity recognition task in the English language as a case study and explore style transfer as a data augmentation method to increase the …...

AbstractVarious historical languages, which used to be lingua franca of science and arts, deserve the attention of current NLP research. In this work, we take the first data-driven steps towards …...

AbstractUncertainty estimation (UE) of model predictions is a crucial step for a variety of tasks such as active learning, misclassification detection, adversarial attack detection, out-of-distribution detection, etc. Most of the …...

AbstractBoundary information is critical for various Chinese language processing tasks, such as word segmentation, part-of-speech tagging, and named entity recognition. Previous studies usually resorted to the use of a high-quality …...

AbstractNamed Entity Recognition (NER) is a fundamental task in information extraction that locates the mentions of named entities and classifies them in unstructured texts. Previous studies typically used hidden Markov …...

AbstractChemXtraxt main goal is to extract the chemical events from patent documents. Event extraction requires that we first identify the names of chemical compounds involved in the events. Thus, in …...

AbstractThis study presents a diverse set of freely available linguistic resources for Turkish natural language processing, including corpora, pretrained models and education material. Although Turkish is spoken by a sizeable …...

AbstractWe conducted a human subject study of named entity recognition on a noisy corpus of conversational music recommendation queries, with many irregular and novel named entities. We evaluated the human …...

AbstractMost weakly supervised named entity recognition (NER) models rely on domain-specific dictionaries provided by experts. This approach is infeasible in many domains where dictionaries do not exist. While a phrase …...

AbstractDeep neural models for named entity recognition (NER) have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as …...

AbstractIn this article, which was prepared for the sameval2023 competition (task number 2), information about the implementation techniques of the transformer model and the use of the pre-trained BERT model …...

AbstractWe present bgGLUE (Bulgarian General Language Understanding Evaluation), a benchmark for evaluating language models on Natural Language Understanding (NLU) tasks in Bulgarian. Our benchmark includes NLU tasks targeting a variety …...

AbstractIn sensitive domains, the sharing of corpora is restricted due to confidentiality, copyrights or trade secrets. Automatic text generation can help alleviate these issues by producing synthetic texts that mimic …...

AbstractSome Natural Language Processing (NLP) tasks that are in the sufficiently solved state for general domain English still struggle to attain the same level of performance in specific domains. Named …...

AbstractEntity Linking performance has a strong reliance on having a large quantity of high-quality annotated training data available. Yet, manual annotation of named entities, especially their boundaries, is ambiguous, error-prone, …...

In this research, we utilized the training dataset from the ROCLING 2023 Chinese Multi-genre Named Entity Recognition in the Healthcare Domain, which comprises the Chi- nese HealthNER Corpus (Lee and …...

AbstractWe evaluate the effectiveness of using data augmentation to improve the generalizability of a Named Entity Recognition model for the task of medication identification in clinical notes. We compare disparate …...

AbstractThis paper describes our submission to SemEval-2023 Task 6, Subtask B, a shared task on performing Named Entity Recognition in legal documents for specific legal entity types. Documents are divided …...

A cancer registry is a critical database for cancer research, which require diverse domain knowledge and manual extraction of vital information from patient records for surveillance. In order to building …...

AbstractNamed Entity Recognition (NER) is a core natural language processing task in which pre-trained language models have shown remarkable performance. However, standard benchmarks like CoNLL 2003 do not address many …...