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We describe and experimentally evaluate an efficient method for automatically de- termining small clause boundaries in spon- taneous speech. Our method applies an ar- tificial neural network to information about …...

Grammatical relationships are an important level of natural language processing. We present a trainable approach to find these relationships through transfor- mation sequences and-error-driven learning. Our ap- proach finds grammatical …...

This paper proposes a series of techniques for ex- tracting English verb–particle constructions from raw text corpora. We initially propose three basic methods, based on tagger output, chunker output and …...

We present a stochastic parsing system consisting of a Lexical-Functional Gram- mar (LFG), a constraint-based parser and a stochastic disambiguation model. We re- port on the results of applying this …...

We train a decision tree inducer (CART) and a memory-based classifier (MBL) on predicting prosodic pitch accents and breaks in Dutch text, on the basis of shal- low, easy-to-compute features. …...

We propose the use of Lexicalized Tree Adjoining Grammar (LTAG) as a source of features that are useful for reranking the output of a statistical parser. In this paper, we …...

As part of its description of lexico-semantic predicate frames or conceptual structures, the FrameNet project defines a set of semantic roles specific to the core predicate of a sentence. Recently, …...

Recent work on spoken document retrieval has suggested that it is adequate to take the single- best output of ASR, and perform text retrieval on this output. This is reasonable …...

. Anaphora resolution is one of essential tasks in message understanding. In this paper resolution for pronominal and sortal anaphora, which are common in biomedical texts, is addressed. The resolution …...

We describe a statistical approach to semantic role labelling that employs only shallow infor- mation. We use a Maximum Entropy learner, augmented by EM-based clustering to model the fit between …...

This paper investigates the usefulness of sentence-internal prosodic cues in syntac- tic parsing of transcribed speech. Intu- itively, prosodic cues would seem to pro- vide much the same information in …...

Discriminative reranking is one method for constructing high-performance statis- tical parsers (Collins, 2000). A discrim- inative reranker requires a source of can- didate parses for each sentence. This pa- per …...

In this paper, we propose a method that exploits full parsing information by repre- senting it as features of argument classifi- cation models and as constraints in integer linear learning …...

This paper presents the first probabilistic parsing results for French, using the re- cently released French Treebank. We start with an unlexicalized PCFG as a base- line model, which is …...

This paper presents an effective approach for resume information extraction to support automatic resume management and routing. A cascaded information extraction (IE) framework is designed. In the first pass, a …...

This paper considers the popular but ques- tionable technique of ‘round-trip transla- tion’ (RTT) as a means of evaluating free on-line Machine Translation systems. Two experiments are reported, both relat- …...

. Biomedical named entity recognition (NER) is a difficult problem in biomedical information processing due to the widespread am- biguity of terms out of context and extensive lexical variations. This …...

In this paper we introduce an empirical approach to the semantic interpretation of superlative adjectives. We present a cor- pus annotated for superlatives and pro- pose an interpretation algorithm that …...

We propose a solution to the annotation bottleneck for statistical parsing, by ex- ploiting the lexicalized nature of Combi- natory Categorial Grammar (CCG). The parsing model uses predicate-argument dependencies for …...

. This paper presents a hybrid method for extracting Chinese noun phrase collocations that combines a statistical model with rule-based linguistic knowledge. The algorithm first extracts all the noun phrase …...

The present methodology for evaluating complex questions at TREC analyzes an- swers in terms of facts called “nuggets”. The official F-score metric represents the harmonic mean between recall and pre- …...

We consider the problem of predictive infer- ence for probabilistic binary sequence label- ing models under F-score as utility. For a simple class of models, we show that the number …...

Standard approaches to Chinese word seg- mentation treat the problem as a tagging task, assigning labels to the characters in the sequence indicating whether the char- acter marks a word …...

This paper proposes the use of Lexical- ized Tree-Adjoining Grammar (LTAG) for- malism as an important additional source of features for the Semantic Role Labeling (SRL) task. Using a set …...

A key question facing the parsing commu- nity is how to compare parsers which use different grammar formalisms and produce different output. Evaluating a parser on the same resource used …...

Standard pairwise coreference resolution systems are subject to errors resulting from their performing anaphora identifi- cation as an implicit part of coreference resolution. In this paper, we propose an integer …...

It has been widely observed that different NLP appli- cations require different sense granularities in order to best exploit word sense distinctions, and that for many applications WordNet senses are …...

In this paper, we described the PNNL Word Sense Disambiguation system as ap- plied to the English all-word task in Se- mEval 2007. We use a supervised learning approach, employing …...

To date, work on Non-Local Dependencies (NLDs) has focused almost exclusively on English and it is an open research question how well these approaches migrate to other languages. This paper …...

AbstractSome time in the future, some spelling error correction system will correct all the errors, and only the errors. We need evaluation metrics that will tell us when this has …...

Event reference identification is often treated as a sentence level classification task. However, several different event ref- erences can occur within a single sentence. We present a set of experiments …...

Manually maintaining comprehensive databases of multi-word expressions, for example Verb-Particle Constructions (VPCs), is infeasible. We describe a new type level classifier for potential VPCs, which uses information in the Google …...

Discriminative feature-based methods are widely used in natural language processing, but sentence parsing is still dominated by gen- erative methods. While prior feature-based dynamic programming parsers have restricted training and …...

This paper studies textual inference by inves- tigating comma structures, which are highly frequent elements whose major role in the ex- traction of semantic relations has not been hitherto recognized. …...

Conventional n-best reranking techniques of- ten suffer from the limited scope of the n- best list, which rules out many potentially good alternatives. We instead propose forest reranking, a method …...

We develop a data set of Malay lexemes la- belled with count classifiers, that are attested in raw or lemmatised corpora. A maximum entropy classifier based on simple, language- inspecific …...

. This paper reports about a multi-engine approach for the development of a NER system in Bengali by combining the classifiers such as Maximum Entropy (ME), Conditional Random Field (CRF) …...

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, …...

. As a key factor in English grammar checking, subject-verb agreement evaluation plays an important part in assessing translated English texts. In this paper, we propose a hybrid method for …...

The accuracy of named entity recognition sys- tems relies heavily upon the volume and qual- ity of available training data. Improving the process of automatically producing such train- ing data …...

One of the reasons nonparametric Bayesian inference is attracting attention in computa- tional linguistics is because it provides a prin- cipled way of learning the units of generaliza- tion together …...

Instance sampling is a method to balance ex- tremely skewed training sets as they occur, for example, in machine learning settings for anaphora resolution. Here, the number of nega- tive …...

Much NLP research on Multi-Word Ex- pressions (MWEs) focuses on the discov- ery of new expressions, as opposed to the identification in texts of known expres- sions. However, MWE identification …...

Many named entities contain other named entities inside them. Despite this fact, the field of named entity recognition has al- most entirely ignored nested named en- tity recognition, but due …...

The present paper is concerned with sta- tistical parsing of constituent structures in German. The paper presents four ex- periments that aim at improving parsing performance of coordinate structure: 1) …...

Vector space models of word meaning typi- cally represent the meaning of a word as a vec- tor computed by summing over all its corpus occurrences. Words close to this …...

Although a lot of progress has been made recently in word segmentation and POS tagging for Chinese, the output of cur- rent state-of-the-art systems is too inaccu- rate to allow …...

Information is fundamental to Finance, and un- derstanding how it flows from official sources to news agencies is a central problem. Readers need to digest information rapidly from high vol- …...

This paper presents three successful tech- niques to translate prepositions heading verbal complements by means of rich lin- guistic information, in the context of a rule-based Machine Translation system for …...

This paper employs morphological struc- tures and relations between sentence seg- ments for opinion analysis on words and sentences. Chinese words are classified into eight morphological types by two proposed …...

We evaluate the Berkeley parser on text from an online discussion forum. We evaluate the parser output with and without gold tokens and spellings (using Sparseval and Parseval), and we …...

The character-based tagging approach is a dominant technique for Chinese word segmentation, and both discrimi- native and generative models can be adopted in that framework. However, generative and discriminative charac- …...

AbstractThis paper proposes a simple and fast person-name filter, which plays an important role in automatic compilation of a large bilingual person-name lexicon. This filter is based on pn_score, which …...

While Inversion Transduction Grammar (ITG) has regained more and more attention in recent years, it still suffers from the major obstacle of speed. We propose a discriminative ITG prun- ing …...

AbstractThis paper describes a hybrid system (FrAG) for tagging / parsing French text, and presents results from ongoing development work, corpus annotation and evaluation. The core of the system is …...

In this paper, we describe HeidelTime, a system for the extraction and normaliza- tion of temporal expressions. HeidelTime is a rule-based system mainly using regu- lar expression patterns for the …...

AbstractClinical texts contain a large amount of information. Some of this information is embedded in contexts where e.g. a patient status is reasoned about, which may lead to a considerable …...

We introduce a novel training algorithm for unsupervised grammar induction, called Zoomed Learning. Given a training set T and a test set S, the goal of our algorithm is to …...

Pathology reports are used to store infor- mation about cells and tissues of a patient, and they are crucial to monitor the health of individuals and population groups. In this …...

AbstractWe present a partial dependency parser for Irish. Constraint Grammar (CG) based rules are used to annotate dependency relations and grammatical functions. Chunking is performed using a regular-expression grammar which …...

We describe the SEERLAB system that participated in the SemEval 2010’s Keyphrase Extraction Task. SEERLAB utilizes the DBLP corpus for generating a set of candidate keyphrases from a document. Random …...

We study self-training with products of latent variable grammars in this paper. We show that increasing the quality of the automatically parsed data used for self-training gives higher accuracy self-trained …...

For millions of people in less resourced re- gions of the world, text messages (SMS) pro- vide the only regular contact with their doc- tor. Classifying messages by medical labels …...

AbstractIn this paper we present LX-Parser, a probabilistic, robust constituency parser for Portuguese. This parser achieves ca. 88% f-score in the labeled bracketing task, thus reaching a state-of-the-art performance score …...

We present and evaluate the first method known to us that can create rich non- extract-based opinion summaries from general text (e.g. newspaper articles). We first describe two possible representations …...

The computation of logical form has been pro- posed as an intermediate step in the translation of sentences to logic. Logical form encodes the resolution of scope ambiguities. In this …...

This paper investigates improving supervised word segmentation accuracy with unlabeled data. Both large-scale in-domain data and small-scale document text are considered. We present a unified solution to include features derived …...

Machine learning approaches for Information Extraction use different types of features to acquire semantically related terms from free text. These features may contain several kinds of linguistic knowledge: from orthographic …...

State-of-the-art statistical machine translation (MT) systems have made significant progress towards producing user-acceptable translation output. However, there is still no efficient way for MT systems to inform users which words …...

Unrehearsed spoken language often contains disfluencies. In order to correctly inter- pret a spoken utterance, any such disfluen- cies must be identified and removed or other- wise dealt with. Operating …...

AbstractThe present paper explores a wide range of word sense disambiguation (WSD) algorithms for German. These WSD algorithms are based on a suite of semantic relatedness measures, including path-based, information-content-based, …...

AbstractVector space models benefit from using an outside corpus to train the model. It is, however, unclear what constitutes a good training corpus. We have investigated the effect on summary …...

Extracting biomedical named entities is one of the major challenges in automatic processing of biomedical literature. This paper proposes a machine learning approach for finding phenotype names in text. Features …...

AbstractMotivation: Gold Standards for named entities are, ironically, not standard themselves. Some specify the “one perfect annotation”. Others specify “perfectly good alternatives”. The concept of Silver standard is relatively new. …...

AbstractIn this paper we present ConanDoyle-neg, a corpus of stories by Conan Doyle annotated with negation information. The negation cues and their scope, as well as the event or property …...

Many parsers learn sparse class distributions over trees to model natural language. Recursive Neural Networks (RNN) use much denser representations, yet can still achieve an F-score of 92.06% for right …...

Scientific publications contain many references to method terminologies used during scientific experiments. New terms are constantly created within the research community, especially in the biomedical domain where thousands of papers …...

Constituency parser performance is primarily interpreted through a single metric, F-score on WSJ section 23, that conveys no linguis- tic information regarding the remaining errors. We classify errors within a …...

We investigate the stacking of dependency and phrase structure parsers, i.e. we define features from the output of a phrase structure parser for a dependency parser and vice versa. Our …...

There are many languages considered to be low-density languages, either because the population speaking the language is not very large, or because insufficient digitized text material is available in the …...

We apply Combinatory Categorial Grammar to wide-coverage parsing in Chinese with the new Chinese CCGbank, bringing a formalism capable of transparently recovering non-local dependencies to a language in which they …...

Unsupervised Relation Extraction (URE) is the task of extracting relations of a priori unknown semantic types using clustering methods on a vector space model of entity pairs and patterns. In …...

In this paper, we propose a weakly supervised system, YouCat, for categorizing Youtube videos into different genres like Comedy, Horror, Romance, Sports and Technology The system takes a Youtube video …...

This paper describes University of Leipzig’s approach to SemEval-2013 task 2B on Sen- timent Analysis in Twitter: message polar- ity classification. Our system is designed to function as a baseline, …...

In this paper, we propose a multi-step stacked learning model for disfluency detection. Our method incorporates refined n-gram features step by step from different word sequences. First, we detect filler …...

Nowadays supervised sequence labeling models can reach competitive performance on the task of Chinese word segmenta- tion. However, the ability of these mod- els is restricted by the availability of …...

This paper presents the Tweetsted system im- plemented for the SemEval 2013 task on Sen- timent Analysis in Twitter. In particular, we participated in Task B on Message Polar- ity …...

We present a method for automatically generating input parsers from English specifications of input file formats. We use a Bayesian generative model to cap- ture relevant natural language phenomena and …...

AbstractIn this paper, we present the first incremental parser for Tree Substitution Grammar (TSG). A TSG allows arbitrarily large syntactic fragments to be combined into complete trees; we show how …...

This paper describes the NILC USP system that participated in SemEval-2013 Task 2: Sentiment Analysis in Twitter. Our system adopts a hybrid classification process that uses three classification approaches: rule- …...

In this paper, we describe how we created two state-of-the-art SVM classifiers, one to de- tect the sentiment of messages such as tweets and SMS (message-level task) and one to …...

This paper is to introduce our participation in the WMT13 shared tasks on Quality Estima- tion for machine translation without using ref- erence translations. We submitted the results for Task …...

We evaluate a naive machine learning ap- proach to sentiment classification focused on Twitter in the context of the sentiment analysis task of SemEval-2013. We employ a classifier based on …...

This paper describes the system developed by the Serendio team for the SemEval-2013 Task 2 competition (Task A). We use a lexicon based approach for discovering sentiments. Our lexicon is …...

This paper presents our system, TJP, which participated in SemEval 2013 Task 2 part A: Contextual Polarity Disambiguation. The goal of this task is to predict whether marked con- texts …...

In this paper, we present our approach to SemEval-2013 Task 9.2. It is a feature rich classification using LIBSVM for Drug-Drug Interactions detection in the BioMedical do- main. The features …...

In this work we present sentiment analysis of messag- es posted on a medical forum. We categorize posts, written in English, into five categories: encourage- ment, gratitude, confusion, facts, and …...

In this paper, we explore a novel bilin- gual word alignment approach based on DNN (Deep Neural Network), which has been proven to be very effective in var- ious machine …...

The informal nature of social media text renders it very difficult to be automati- cally processed by natural language pro- cessing tools. Text normalization, which corresponds to restoring the non-standard …...

AbstractSemantic parsing is the task of translating natural language utterances into a machine-interpretable meaning representation. Most approaches to this task have been evaluated on a small number of existing corpora …...

Most studies on statistical Korean word spac- ing do not utilize the information provided by the input sentence and assume that it was completely concatenated. This makes the word spacer …...

AbstractWe present the CLiPS Stylometry Investigation (CSI) corpus, a new Dutch corpus containing reviews and essays written by university students. It is designed to serve multiple purposes: detection of age, …...

Event extraction is a popular research topic in natural language processing. Several event extrac- tion tasks have been defined for both the newswire and biomedical domains. In general, different systems …...

We present a novel approach for automatic report generation from time-series data, in the context of student feedback genera- tion. Our proposed methodology treats content selection as a multi-label (ML) …...

AbstractWe construct a large corpus of Japanese predicate phrases for synonym-antonym relations. The corpus consists of 7,278 pairs of predicates such as “receive-permission (ACC)” vs. “obtain-permission (ACC)”, in which each …...

AbstractCoreference resolution (CR) is a current problem in natural language processing (NLP) research and it is a key task in applications such as question answering, text summarization and information extraction …...

AbstractWe attempt to identify citations in non-academic text such as patents. Unlike academic articles which often provide bibliographies and follow consistent citation styles, non-academic text cites scientific research in a …...

We propose a simple unsupervised ap- proach to detecting non-compositional components in multiword expressions based on Wiktionary. The approach makes use of the definitions, synonyms and trans- lations in Wiktionary, …...

Analogies are considered to be one of the core concepts of human cognition and communica- tion, and are very efficient at encoding com- plex information in a natural fashion. How- …...

The shared task on Aspect based Senti- ment Analysis primarily focuses on mining relevant information from the thousands of online reviews available for a popular product or service. In this …...

In this paper we report our works for SemEval-2014 Sentiment Analysis Twitter evaluation challenge. This is thein first time we attempt for this task, and our submissions are based on …...

This paper presents the results of the Ix- aMed team at the SemEval-2014 Shared Task 7 on Analyzing Clinical Texts. We have developed three different sys- tems based on: a) …...

We present a weakly supervised approach for learning hashtags, hashtag patterns, and phrases associated with five emotions: AFFEC- TION, ANGER/RAGE, FEAR/ANXIETY, JOY, and SADNESS/DISAPPOINTMENT. Starting with seed hashtags to label …...

Semantic hierarchy construction aims to build structures of concepts linked by hypernym–hyponym (“is-a”) relations. A major challenge for this task is the automatic discovery of such relations. This paper proposes …...

AbstractIn this paper, we describe how a Constraint Grammar with linguist-written rules can be optimized and ported to another language using a Machine Learning technique. The effects of rule movements, …...

AbstractEarly detection of suicidal thoughts is an important part of effective suicide prevention. Such thoughts may be expressed online, especially by young people. This paper presents on-going work on the …...

Information Extraction using multiple information sources and systems is beneficial due to multi- source/system consolidation and challenging due to the resulting inconsistency and redundancy. We integrate IE and truth-finding research …...

The TJP system is presented, which partici- pated in SemEval 2014 Task 9, Part A: Contextual Polarity Disambiguation. Our system is ‘constrained’, using only data provided by the organizers. The …...

AbstractWe present novel computational experiments using William Labov’s theory of narrative analysis. We describe his six elements of narrative structure and construct a new corpus based on his most recent …...

AbstractWe show how to use large biomedical databases in order to obtain a gold standard for training a machine learning system over a corpus of biomedical text. As an example …...

Interpreting event mentions in text is cen- tral to many tasks from scientific research to intelligence gathering. We present an event trigger detection system and explore baseline configurations. Specifically, we …...

This paper presents a statistical model for measuring structural similarity between webpages from bilingual websites. Start- ing from basic assumptions we derive the model and propose an algorithm to esti- …...

In this paper we focus on a new problem of event coreference resolution across televi- sion news videos. Based on the observa- tion that the contents from multiple data modalities …...

This paper presents a Constraint Grammar- based pedagogical proofing tool for Danish. The system recognizes not only spelling errors, but also grammatical errors in otherwise correctly spelled words, and categorizes …...

In the current era of online interactions, both positive and negative experiences are abundant on the Web. As in real life, neg- ative experiences can have a serious im- pact …...

We present a discriminative model for de- tecting disfluencies in spoken language tran- scripts. Structurally, our model is a semi- Markov conditional random field with features targeting characteristics unique to …...

This paper describes the approach used by ezDI at the SemEval 2015 Task-14: ”Anal- ysis of Clinical Text”. The task was di- vided into two embedded tasks. Task-1 re- quired …...

This paper introduces a tensor-based approach to semantic role labeling (SRL). The motiva- tion behind the approach is to automatically induce a compact feature representation for words and their relations, …...

This paper describes the system developed by our team (HLTC-HKUST) for task 1 of Se- mEval 2015 workshop about paraphrase clas- sification and semantic similarity in Twitter. We trained a …...

We present a multi-task learning approach that jointly trains three word alignment mod- els over disjoint bitexts of three languages: source, target and pivot. Our approach builds upon model triangulation, …...

After the Great East Japan Earthquake in 2011, an abundance of false rumors were dis- seminated on Twitter that actually hindered rescue activities. This work presents a method for recognizing …...

This paper describes a sentiment classifica- tion system designed for SemEval-2015, Task 10, Subtask B. The system employs a con- strained, supervised text categorization ap- proach. Firstly, since thorough preprocess- …...

In this paper, we describe our approach to Se- meval 2015 task 10 subtask B, message level sentiment detection. Our system implements a variety of classifiers and data preparation tech- …...

We developed a system to participate in shared tasks on the analyzing clinical text. Our system approaches are both machine learning-based and rule-based. We applied the machine learning-based approach for …...

We present an approach to identifying Twitter paraphrases using simple lexical over- lap features. The work is part of ongoing re- search into the applicability of knowledge- lean techniques to …...

SpaceEval (SemEval 2015 Task 8), which concerns spatial information extraction, builds on the spatial role identification tasks intro- duced in SemEval 2012 and used in SemEval 2013. Among the host …...

We propose a first-ever attempt to employ a Long Short-Term memory based framework to predict humor in dialogues. We analyze data from a popular TV-sitcom, whose canned laughters give an …...

When a large-scale incident or disaster oc- curs, there is often a great demand for rapidly developing a system to extract detailed and new information from low- resource languages (LLs). …...

AbstractIn this paper, we study the problem of disfluency detection using the encoder-decoder framework. We treat disfluency detection as a sequence-to-sequence problem and propose a neural attention-based model which can …...

Dropped Pronouns (DP) in which pronouns are frequently dropped in the source language but should be retained in the target language are challenge in machine translation. In re- sponse to …...

AbstractThis paper addresses the problem of quantifying the differences between entity extraction systems, where in general only a small proportion a document should be selected. Comparing overall accuracy is not …...

The goal of Event extraction is to extract struc- tured information of events that are of interest from unstructured documents. Existing event extractors for social media suffer from two major …...

We present a supervised system that uses lex- ical, sentiment, semantic dictionaries and la- tent and frame semantic features to identify the stance of a tweeter towards an ideologi- cal …...

AbstractThe paper applies a deep recurrent neural network to the task of sentence boundary detection in Sanskrit, an important, yet underresourced ancient Indian language. The deep learning approach improves the …...

AbstractGreedy transition-based parsers are appealing for their very fast speed, with reasonably high accuracies. In this paper, we build a fast shift-reduce neural constituent parser by using a neural network …...

AbstractThis paper presents a method for the normalization of historical texts using a combination of weighted finite-state transducers and language models. We have extended our previous work on the normalization …...

The recently proposed coupled sequence label- ing is shown to be able to effectively exploit multiple labeled data with heterogeneous an- notations but suffer from severe inefficiency problem due to …...

We focus on two leading state-of-the-art approaches to grammatical error correc- tion – machine learning classification and machine translation. Based on the com- parative study of the two learning frame- …...

This paper is a novel study that views sarcasm detection in dialogue as a sequence labeling task, where a dialogue is made up of a se- quence of utterances. We …...

This paper describes the system developed for the task of temporal information extraction from clinical narratives in the context of 2016 Clinical TempEval challenge. Clinical TempEval 2016 addressed the problem …...

We describe our submission system to the SemEval-2016 Task 8 on Abstract Meaning Representation (AMR) Parsing. We attempt to improve AMR parsing by exploiting prepo- sition semantic role labeling information …...

This paper describes the system submitted by IHS-RD-Belarus team for the sentiment detec- tion polarity subtask on Aspect Based Senti- ment Analysis task at the SemEval 2016 workshop on semantic …...

This paper proposes a method for intra- sentential subject zero anaphora resolution in Japanese. Our proposed method utilizes a Multi-column Convolutional Neural Network (MCNN) for predicting zero anaphoric rela- tions. …...

AbstractWe present a dependency to constituent tree conversion technique that aims to improve constituent parsing accuracies by leveraging dependency treebanks available in a wide variety in many languages. The technique …...

Sentiments expressed in user-generated short text and sentences are nuanced by subtleties at lexical, syntactic, semantic and pragmatic levels. To address this, we propose to augment traditional features used for …...

AbstractWe introduce a generic Language Independent Framework for Linguistic Code Switch Point Detection. The system uses characters level 5-grams and word level unigram language models to train a conditional random …...

Metaphorical expressions are pervasive in natural language and pose a substan- tial challenge for computational seman- tics. The inherent compositionality of metaphor makes it an important test case for compositional …...

We present the description of the LTG entry in the SemEval-2016 Complex Word Identi- fication (CWI) task, which aimed to develop systems for identifying complex words in En- glish sentences. …...

AbstractChinese sentences are written as sequences of characters, which are elementary units of syntax and semantics. Characters are highly polysemous in forming words. We present a position-sensitive skip-gram model to …...

Modern question answering and summa- rizing systems have motivated the need for complex n-ary relation extraction sys- tems where the number of related entities (n) can be more than two. …...

In this paper we tackle a challenging name tagging problem in an emergent setting - the tagger needs to be complete within a few hours for a new incident language …...

We introduce the task of cross-lingual lexical entailment, which aims to detect whether the meaning of a word in one language can be in- ferred from the meaning of a …...

AbstractStreaming media provides a number of unique challenges for computational linguistics. This paper studies the temporal variation in word co-occurrence statistics, with application to event detection. We develop a spectral …...

AbstractWord Sense Disambiguation (WSD) is one of the open problems in the area of natural language processing. Various supervised, unsupervised and knowledge based approaches have been proposed for automatically determining …...

In timeline extraction the goal is to order all the events in which a target entity is involved in a timeline. Due to the lack of explic- itly annotated data, …...

Slot filling aims to extract the values (slot fillers) of specific attributes (slots types) for a given entity (query) from a large- scale corpus. Slot filling remains very challenging over …...

Semantic deficit is a symptom of language impairment in Alzheimer’s disease (AD). We present a generalizable method for au- tomatic generation of information content units (ICUs) for a picture used …...

AbstractSentiment analysis is the computational task of extracting sentiment from a text document – for example whether it expresses a positive, negative or neutral opinion. Various approaches have been introduced …...

AbstractWe present a deterministic sieve-based system for attributing quotations in literary text and a new dataset: QuoteLi3. Quote attribution, determining who said what in a given text, is important for …...

Automatic document classification is now a growing research topic in Natural Language Processing. Several techniques were incorporated to build a classifier that can categorize documents written in specific languages into …...

AbstractPunctuation and segmentation is crucial in spoken language translation, as it has a strong impact to translation performance. However, the impact of rare or unknown words in the performance of …...

Under the issue of gender and Natural Language Processing (NLP), most papers aim at gender- norm language that spoken by biologically males and females with opposite-sex desires. However, from the …...

AbstractThis paper describes the system developed for the task of temporal information extraction from clinical narratives in the context of the 2017 Clinical TempEval challenge. Clinical TempEval 2017 addressed the …...

AbstractIdea Density (ID) measures the rate at which ideas or elementary predications are expressed in an utterance or in a text. Lower ID is found to be associated with an …...

AbstractThis paper describes our system, entitled Idiom Savant, for the 7th Task of the Semeval 2017 workshop, “Detection and interpretation of English Puns”. Our system consists of two probabilistic models …...

AbstractSlot Filling (SF) aims to extract the values of certain types of attributes (or slots, such as person:cities_of_residence) for a given entity from a large collection of source documents. In …...

AbstractMetaphor detection has been both challenging and rewarding in natural language processing applications. This study offers a new approach based on eventive information in detecting metaphors by leveraging the Chinese …...

AbstractThis paper describes the improvements that we have applied on CAMR baseline parser (Wang et al., 2016) at Task 8 of SemEval-2016. Our objective is to increase the performance of …...

AbstractThis paper presents our approach to participate in the SemEval 2017 Task 12: Clinical TempEval challenge, specifically in the event and time expressions span and attribute identification subtasks (ES, EA, …...

AbstractIn this paper, we introduce an Abstract Meaning Representation (AMR) to Dependency Parse aligner. Alignment is a preliminary step for AMR parsing, and our aligner improves current AMR parser performance. …...

The rise in accessibility of web to the mass has led to a spurt in the use of social media making it convenient and powerful way to express and exchange …...

AbstractEntity mentions embedded in longer entity mentions are referred to as nested entities. Most named entity recognition (NER) systems deal only with the flat entities and ignore the inner nested …...

We present a new corpus of 200 abstracts and 100 full text papers which have been annotated with named entities and relations in the biomedical domain as part of the …...

Word segmentation, a fundamental technology for lots of downstream applications, plays a significant role in Natural Language Processing, especially for those languages without explicit delimiters, like Chinese, Korean, Japanese and …...

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 …...

Personality is the important internal framework that we use when we communicate with the others; thus, it is critical for vehicle-driver communication. Evaluating an individual’s personality requires associating behavior and …...

Emoji are pictographs commonly used in microblogs as emotion markers, but they can also represent a much wider range of concepts. Additionally, they may occur in different positions within a …...

AbstractWe propose a practical model for named entity recognition (NER) that combines word and character-level information with a specific learned representation of the prefixes and suffixes of the word. We …...

Semantic parsing offers many opportunities to improve natural language understanding. We present a semantically annotated parallel corpus for English, German, Italian, and Dutch where sentences are aligned with scoped meaning …...

AbstractCybersecurity risks such as malware threaten the personal safety of users, but to identify malware text is a major challenge. The paper proposes a supervised learning approach to identifying malware …...

Projects that set out to create a linguistic resource often do so by using a machine learning model that pre-annotates or filters the content that goes through to a human …...

AbstractDetecting changes within an unfolding event in real time from news articles or social media enables to react promptly to serious issues in public safety, public health or natural disasters. …...

AbstractIn this paper, we study the problem of parsing structured knowledge graphs from textual descriptions. In particular, we consider the scene graph representation that considers objects together with their attributes …...

Sentiment analysis has been receiving increasing interest as it conveys valuable information in regard to people’s preferences and opinions. In this work, we present a sentiment analyzer that identifies the …...

This paper describes a pilot study act to investigate the semiotic types of hand gestures in video-recorded speeches and their automatic classification. Gestures, which also comprise e.g. head movements and …...

AbstractWe present a new dataset comprised of 210,532 tokens evenly drawn from 100 different English-language literary texts annotated for ACE entity categories (person, location, geo-political entity, facility, organization, and vehicle). …...

AbstractThis paper presents three hybrid models that directly combine latent Dirichlet allocation and word embedding for distinguishing between speakers with and without Alzheimer’s disease from transcripts of picture descriptions. Two …...

AbstractThe current state of the art for First Story Detection (FSD) are nearest neighbour-based models with traditional term vector representations; however, one challenge faced by FSD models is that the …...

AbstractIn the effort to tackle the challenge of Hyperpartisan News Detection, i.e., the task of deciding whether a news article is biased towards one party, faction, cause, or person, we …...

AbstractWe focus on improving name tagging for low-resource languages using annotations from related languages. Previous studies either directly project annotations from a source language to a target language using cross-lingual …...

AbstractThis work presents an approach decomposing propositions into four functional components and identify the patterns linking those components to determine argument structure. The entities addressed by a proposition are target …...

AbstractThis paper describes novel models tailored for a new application, that of extracting the symptoms mentioned in clinical conversations along with their status. Lack of any publicly available corpus in …...

AbstractPersonal health mention detection deals with predicting whether or not a given sentence is a report of a health condition. Past work mentions errors in this prediction when symptom words, …...

AbstractRecently we proposed the Span Attribute Tagging (SAT) Model to infer clinical entities (e.g., symptoms) and their properties (e.g., duration). It tackles the challenge of large label space and limited …...

AbstractThis paper describes our system, Joint Encoders for Stable Suggestion Inference (JESSI), for the SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums. JESSI is a combination of …...

AbstractSarcasm is often expressed through several verbal and non-verbal cues, e.g., a change of tone, overemphasis in a word, a drawn-out syllable, or a straight looking face. Most of the …...

AbstractShort-text classification is a challenging task, due to the sparsity and high dimensionality of the feature space. In this study, we aim to analyze and classify Turkish tweets based on …...

AbstractIt has been widely admitted that morphological analysis is an important step in automated text processing for morphologically rich languages. Evenki is a language with rich morphology, therefore a morphological …...

AbstractWe conduct a linguistic analysis of recent metaphor recognition systems, all of which are based on language models. We show that their performance, although reaching high F-scores, has considerable gaps …...

AbstractThis paper presents the German Twitter section of a large (2 billion word) bilingual Social Media corpus for Hate Speech research, discussing the compilation, pseudonymization and grammatical annotation of the …...

AbstractIn this paper, we propose a full pipeline of analysis of a large corpus about a century of public meeting in historical Australian news papers, from construction to visual exploration. …...

AbstractCognates are variants of the same lexical form across different languages; for example “fonema” in Spanish and “phoneme” in English are cognates, both of which mean “a unit of sound”. …...

AbstractWe present in this paper our work on Algerian language, an under-resourced North African colloquial Arabic variety, for which we built a comparably large corpus of more than 36,000 code-switched …...

AbstractThis paper reports on a parsing system for Wolof based on the LFG formalism. The parser covers core constructions of Wolof, including noun classes, cleft, copula, causative and applicative sentences. …...

AbstractShowing items that do not match search query intent degrades customer experience in e-commerce. These mismatches result from counterfactual biases of the ranking algorithms toward noisy behavioral signals such as …...

AbstractWe explore the task of sentiment analysis on Hinglish (code-mixed Hindi-English) tweets as participants of Task 9 of the SemEval-2020 competition, known as the SentiMix task. We had two main …...

AbstractDeep learning-based Chinese zero pronoun resolution model has achieved better performance than traditional machine learning-based model. However, the existing work related to Chinese zero pronoun resolution has not yet well …...

AbstractState of the art research for date-time entity extraction from text is task agnostic. Consequently, while the methods proposed in literature perform well for generic date-time extraction from texts, they …...

AbstractThe article describes a fast solution to propaganda detection at SemEval-2020 Task 11, based on feature adjustment. We use per-token vectorization of features and a simple Logistic Regression classifier to …...

AbstractAutomatic identification of multiword expressions (MWEs), like ‘to cut corners’ (to do an incomplete job), is a pre-requisite for semantically-oriented downstream applications. This task is challenging because MWEs, especially verbal …...

AbstractWritten communication is of utmost importance to the progress of scientific research. The speed of such development, however, may be affected by the scarcity of reviewers to referee the quality …...

We extract definitions from text books and sci- entific publications on mathematics in both, German and English, from the sub-domain of graph theory. Mathematical texts differ from other domains because …...

AbstractBiomedical Information Extraction from scientific literature presents two unique and non-trivial challenges. First, compared with general natural language texts, sentences from scientific papers usually possess wider contexts between knowledge elements. …...

AbstractToxic spans detection is an emerging challenge that aims to find toxic spans within a toxic text. In this paper, we describe our solutions to tackle toxic spans detection. The …...

AbstractThis paper presents the NICT Kyoto submission for the WMT’21 Quality Estimation (QE) Critical Error Detection shared task (Task 3). Our approach relies mainly on QE model pretraining for which …...

AbstractScripts capture commonsense knowledge about everyday activities and their participants. Script knowledge proved useful in a number of NLP tasks, such as referent prediction, discourse classification, and story generation. A …...

AbstractThis paper evaluates normalization procedures of Persian text for a downstream NLP task - multiword expressions (MWEs) discovery. We discuss the challenges the Persian language poses for NLP and evaluate …...

AbstractHate speech expressions in social media are not limited to textual messages; they can appear in videos, images, or multimodal formats like memes. Existing work towards detecting such expressions has …...

AbstractThe task of modal dependency parsing aims to parse a text into its modal dependency structure, which is a representation for the factuality of events in the text. We design …...

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 …...

AbstractEven though many recent semantic parsers are based on deep learning methods, we should not forget that rule-based alternatives might offer advantages over neural approaches with respect to transparency, portability, …...

AbstractThe act of appearing kind or helpful via the use of but having a feeling of superiority condescending and patronizing language can have have serious mental health implications to those …...

AbstractWe describe our participation on the Multi-evidence Natural Language Inference for Clinical Trial Data (NLI4CT) of SemEval’23. The organizers provided a collection of clinical trials as training data and a …...

AbstractScarcity of data and technological limitations for resource-poor languages in developing countries like India poses a threat to the development of sophisticated NLU systems for healthcare. To assess the current …...

AbstractMany adversarial attacks in NLP perturb text in puts to produce visually similar strings (‘ergo’, ‘εrgo’) which are legible to humans but degrade model performance. Although preserving legibility is a …...

AbstractThis paper investigates a crucial aspect of mental health by exploring the detection of suicidal ideation in spoken phone conversations between callers and counselors at a suicide prevention hotline. These …...

AbstractBiographical event detection is a relevant task that allows for the exploration and comparison of the ways in which people’s lives are told and represented. This may support several real-life …...