Табиғи тілдерді өңдеу

This paper is concerned with the design of a processor capable of formalizing English language descriptions of problems in the sententlal calculus. The emphasis is on the design of a …...

This paper attempts to systematize natural language analysis process by (I) use of a partitioned semantic network formalism as the meaning representation and (2) stepwise translation based on Montague Grammar. …...

The paper begins by defining a class of distributed memory machines which have useful properties as retrieval and filtering devices. These memory mechanisms store large numbers of associations on a …...

We present a progress report on our research on nominal compounds (NC's). Recent approaches to this probiem in linguistics and natural ianguage processing (NLP) are reviewed and criticized. We argue …...

Kamp's Discourse Representation Theory is a major breakthrough regarding the systematic translation of natural language discourse into logical form. We have therefore chosen to marry the User Specialty Languages System, …...

Computational neurolinguistics (CN) is an approach to computational linguistics which in- cludes neurally-motivated constraints in the design of models of natural language processing. Furthermore, the knowledge representations in- cluded in …...

In this paper the experience made through the development of a NAtural Language driven Image Generation is discussed. This system is able to imagine a static scene described by means …...

This paper presents a semantically oriented, rule based method for single sentence text generation and discusses its implementation in the Kafka generator. This generator is part of the XCALIBUR natural …...

This paper surveys some issues that arise in the study of the syntax and semantics of natural languages (NL's) and have potential relevance to the automatic recognition, parsing, and translation …...

Nowadays it is common the construction of English understanding systems (interfaces) that soo- ner or later one has to re-use, adapting and conve~ ting them to other natural languages. This …...

In every application that concerns the automatic processing of natural language, the problem of the dictionary size is posed. In this paper , we propose a compression dictionary al~orithm based …...

Determiners play an important role in conveying the meaning of an utterance, but they have often been disregarded, perhaps because it seemed more i~portant to devise methods to grasp the …...

: The paper describes the design of a fair sized lexi- cal database that is to be used with a natural language based expert system with German as the language …...

: Semantic processing is one of the important tasks for natural language processing. Basic to semantic processing is descriptions of lexical items. The most frequently used form of description of …...

As part of DARPA's Strategic Computing Program, we have moved a large natural language system out of the laboratory. This involved: o Delivery of knowledge acquisition software to the Naval …...

This paper describes the SDC PUNDIT, (Prolog UNDerstands Integrated Text), system for processing natural language messages. 1 PUNDIT , written in Prolog, is a highly modular system consisting of distinct …...

s: SAG~ (Sentence Analysis and GEneration system) is an operational parsing and generating system. It is used as a Natural Language Frontend for Esprit project Eeteam-316, whose purpose is to …...

ion is required to construct the appropriate flmctor expressions in semantics. By contrast, the...

Performance in speech recognition systems has progressed to the point where it is now realistic to begin integrating speech with natural language systems to produce spoken language systems. Two factors …...

Although natural language technology has achieved a high degree of domain independence through separating domain-independent modules from domain-dependent knowledge bases, portability, as measured by effort to move from one application …...

A new natural language system, TINA, has been developed for applications involving speech understanding tasks, which integrates key ideas from context free grammars, Augmented Transition Networks (ATN's) [1], and Lexical …...

: Description of a connectionist implementation of an Earley parser. 1....

This paper describes the initial development of a natural language text processor, as the first step in an INRS dialogue-by-voice system. The eventual system will accept natural, spontaneous speech from …...

AbstractThis paper outlines a new architecture for a NLP/MT development environment for the EUROTRA project, which will be fully operational in the 1993-94 time frame. The proposed architecture provides a …...

This paper describes recent work on the Unisys ATIS Spo- ken Language System, and reports benchmark results on nat- ural language, spoken language, and speech recognition. We describe enhancements to …...

This paper describes an implemented program that takes a tagged text corpus and generates a partial list of the subcategorization frames in wtfich each verb occurs. The completeness of the …...

This paper* describes a .al, ural language p~rsi.g algorithm &)r un- resu'icl,ed I,ext which uses a probabilhq-hased scoring funct,iow I,o se- le(:l, I,he "besC: parse of ~/ sent,enos acc:ording t,c~ …...

The resolution of lexical ambiguity is important for most natural language process- ing tasks, and a range of computational techniques have been proposed for its solu- tion. None of these …...

Most of tile research on parsing natnral [allguages has beetl concerned with I",nglish, or wil, h other languages nlOrl)hologically similar Io English. Parsing agglntinat.ive word st, ructures ha.s altracted relatively …...

Development of reusahle dictionaries for NI,P applications requires a carefully designed lexi- cological framework, a lexical acquisition strat- egy, an integrated development toolbox, and facilities to generate dictionaries for client …...

AbstractMachine-readable versions of everyday dictionaries have been seen as a likely source of information for use in natural language processing because they contain an enormous amount of lexical and semantic …...

AbstractCurrent approaches to computational lexicology in language technology are knowledge-based (competence-oriented) and try to abstract away from specific formalisms, domains, and applications. This results in severe complexity, acquisition and reusability …...

AbstractTyped feature formalisms (TFF) play an increasingly important role in NLP and, in particular, in MT. Many of these systems are inspired by Pollard and Sag’s work on Head-Driven Phrase …...

. The Text Encoding Initiative (TEl) is an international project established in 1988 to develop guidelines for the preparation and interchange of electronic texts for research, and to satisfy a …...

We show the application of an optimisation technique to natural language processing: genetic algorithms, thanks to the definition of a data structure called board and a formal distance. The system …...

Toshihisa Tashiro Noriyoshi Uratani Tsuyoshi Morimoto ATR Interpreting Telecommunications Research Labor~tories 2-2 tIikaridai, Seika-cho, Soraku-gun, Kyoto 619-02, JAPAN {tashiro,uratani,morimoto} ~itl.at r.co.jp A part-of-speech tagged corpus is a very hnportant knowledge …...

Lexical pattern matching and text extraction is an essential component of many Natural Language Processing applications. Following the language hierarchy first conceived by Chomsky, it is commonly accepted that simple …...

Without properly constraining recursions in generation, no language system can effectively operate. This is especially so with morphological generation, for each well-formed word must be finite in length and is …...

On the observation of current available voice organizers, all of them accept only voice commands or word-based commands. Using natural spoken language to operate organizer is still a difficult problem. …...

Cross-serial dependencies in Dutdl and Swiss-German are the only known extra- context fi'ee natural language syntactic phenonmna. Psycholinguistie evidence suggests cross-serial orderings tend to be easier to process t, lmn …...

Probabilistic models have been widely used for natural language processing. Part-of-speech tagging, which assigns the most likely tag to each word in a given sentence, is one. of tire problems …...

Semantic representations which are underspecified with respect to, for example, scope, have recently attracted much attention. Most research in this area has focused on treating English language phenomena in a …...

This I)at)cr d(~scrihcs a, SI);ulish l'arl.-ol: Speech (I'()S) l~a.gger which al,i)lies aad extends Ih'ill's a, lguril;hn~ for 'u,.super- vised learuing of ruh',- based l, aggcrs (llrill, 1995). First, we discuss …...

This paper presents a novel multimodal system applied to the setup and control of distributed interactive simulations. We have developed the QuickSet prototype, a pen/voice system running on a hand-held …...

A layered approach to information retrieval permits the inclusion of multiple search en- gines as well as nmltiple databases, with a natural language layer to convert English queries for use …...

Recognizing shallow linguistic patterns, such as ba- sic syntactic relationships between words, is a com~ mon task in applied natural language and text pro- (:essing. Tile common practice for approaching …...

Language modeling is to associate a sequence of words with a priori probability, which is a key part of many natural language applications such as speech recognition and statisticM lna- …...

Any large language processing software relies in its operation on heuristic decisions concerning the strategy of processing. These decisions are usually "hard-wired" into the software in the form of hand- …...

The goal of this paper is to explicate the nature of Chinese nominal semantics, and to create a paradigm for nominal semantics in general that will be useful for natural …...

In this paper we present results on developing ro- bust natural language interfaces by combining shal- low and partial interpretation with dialogue manage- ment. The key issue is to reduce …...

This paper proposes a method to extract proper names and their associated information from web pages for Internet/Intranet users automatically. The information extracted from World Wide Web documents includes proper …...

Knowledge acquisition is a bottleneck in machine translation and many NLP tasks. A method for automatically acquiring translation templates from bilingual corpora is proposed in this paper. Bilingual sentence pairs …...

In some contexts, well-formed natural language cannot be expected as input to information or communication systems. In these contexts, the use of grammar-independent input (sequences of uninflected semantic units like …...

This paper presents the Natural Language Processing-based linguistic analysis tool that we have developed for Japanese as a Second Language teachers. This program, Zero De- tector (ZD), aims to promote …...

While the corpus-based research relies on hu- man annotated corpora, it is often said that a non-negligible amount of errors remain even in frequently used corpora such as Penn Treebank. …...

There is no blank to mark word boundaries in Chinese text. As a result, identifying words is difficult, because of segmentation ambiguities and occurrences of unknown words. Conventionally unknown words …...

Anaphora is a common phenomenon in discourses as well as an important research issue in the applications of natural language processing. In this paper, the anaphora resolution is achieved by …...

Previous works on question classification are based on complex natural language process- ing techniques: named entity extractors, parsers, chunkers, etc. While these ap- proaches have proven to be effective they …...

Word-aligned bilingual corpora are an important knowledge source for many tasks in natural language processing. We improve the well-known IBM alignment models, as well as the Hidden-Markov alignment model using …...

Texts from the medical domain are an important task for natural language processing. This paper investigates the usefulness of a large medical database (the Unified Medical Language System) for the …...

In this paper we describe a method of automati- cally learning domain theories from parsed cor- pora of sentences from the relevant domain and use FSA techniques for the graphical …...

Regular languages are widely used in NLP to- day in spite of their shortcomings. Efficient algorithms that can reliably learn these lan- guages, and which must in realistic applications only …...

The paper presents a new way of accounting for the meaning of verbs in natural languages, using a diagrammatic notation based on the Unified Modeling Language (UML). We will introduce …...

Empirical experience and observations have shown us when powerful and highly tunable classifiers such as maximum en- tropy classifiers, boosting and SVMs are applied to language processing tasks, it is …...

We propose a new hierarchical Bayesian n-gram model of natural languages. Our model makes use of a generalization of the commonly used Dirichlet distributions called Pitman-Yor processes which pro- duce …...

Lexical classes, when tailored to the appli- cation and domain in question, can provide an effective means to deal with a num- ber of natural language processing (NLP) tasks. While …...

AbstractChat language refers to the special human language widely used in the community of digital network chat. As chat language holds anomalous characteristics in forming words, phrases, and non-alphabetical characters, …...

Unification grammars are widely accepted as an expressive means for describing the structure of natural languages. In gen- eral, the recognition problem is undecid- able for unification grammars. Even with …...

The noisy channel model approach is suc- cessfully applied to various natural lan- guage processing tasks. Currently the main research focus of this approach is adaptation methods, how to capture …...

. Research in natural language processing (NLP) has both scientific and technological dimensions. In both cases, it is necessary to evaluate the implemented systems in order to assess the success …...

. The aim of this paper is to present a description of antonym constructions, which is integrated in a formal grammar of spontanous french in order to allow its use …...

The need for syntactically annotated data for use in natural language processing has increased dramatically in recent years. This is true especially for parallel treebanks, of which very few exist. …...

The distance or similarity metric plays an important role in many natural language processing (NLP) tasks. Previous stud- ies have demonstrated the effectiveness of a number of metrics such as …...

Word clustering is a conventional and im- portant NLP task, and the literature has suggested two kinds of approaches to this problem. One is based on the distribu- tional similarity …...

AbstractIn this paper a first implementation of a tool for valence shifting of natural language texts, named Valentino (VALENced Text INOculator), is presented. Valentino can modify existing textual expressions towards …...

While speaking spontaneously, speakers often make errors such as self-correction or false starts which interfere with the successful application of natural language processing techniques like summarization and machine translation to …...

. Named Entity metonymy resolution is a challenging natural langage processing task, which has been recently subject to a growing interest. In this paper, we describe the method we have …...

: Typed lexical relations between terms are indispensable for the tasks realized in NLP, but collecting lexical information is a difficult process. Indeed, when done manually, it requires the competence …...

AbstractIn this paper we present a new FrameNet-based Shallow Semantic Parser. Shallow Semantic Parsing has been a popular Natural Language Processing task since the 2004 and 2005 CoNLL Shared Task …...

AbstractThe modeling of human behavior becomes more and more important due to the increasing popularity of context-aware computing and people-centric mobile applications. Inspired by the principle of action-as-language, we propose …...

AbstractDetermining semantic relatedness between words or concepts is a fundamental process to many Natural Language Processing applications. Approaches for this task typically make use of knowledge resources such as WordNet …...

AbstractThis article discusses the treatment of collocations in the context of a long-term project on the development of multilingual NLP tools. Besides “classical” two-word collocations, we will focus on the …...

Event Anaphora Resolution is an important task for cascaded event template extraction and other NLP study. In this paper, we provide a first systematic study of resolving pronouns to their …...

AbstractWe present a method for acquiring reliable predicate-argument structures from raw corpora for automatic compilation of case frames. Such lexicon compilation requires highly reliable predicate-argument structures to practically contribute to …...

AbstractNatural language processing technology has developed remarkably, but it is still difficult for computers to understand contextual meanings as humans do. The purpose of our work has been to construct …...

AbstractMany natural language processing tasks, including information extraction, question answering and recognizing textual entailment, require analysis of the polarity, focus of polarity, tense, aspect, mood and source of the event …...

Active learning has been applied to dif- ferent NLP tasks, with the aim of limit- ing the amount of time and cost for human annotation. Most studies on active learn- …...

AbstractLarge corpora are essential to modern methods of computational linguistics and natural language processing. In this paper, we describe an ongoing project whose aim is to build a largest corpus …...

AbstractTextual entailment has been recognized as a generic task that captures major semantic inference needs across many natural language processing applications. However, to date, textual entailment has not been considered …...

AbstractIn this paper we want to point out some issues arising when a natural language processing task involves several languages (like multi- lingual, multidocument summarization and the machine translation aspects …...

AbstractThis article presents the use of NLP techniques (text mining, text analysis) to develop specific tools that allow to create linguistic resources related to the cultural heritage domain. The aim …...

Strategic business decision making in- volves the analysis of market forecasts. Today, the identification and aggregation of relevant market statements is done by human experts, often by analyzing doc- uments …...

AbstractThe paper presents an approach for constructing a weighted bilingual dictionary of inflectional forms using as input data a traditional bilingual dictionary, and not parallel corpora. An algorithm is developed …...

AbstractWe present an experimental framework for Entity Mention Detection in which two different classifiers are combined to exploit Data Redundancy attained through the annotation of a large text corpus, as …...

AbstractEvaluating complex Natural Language Processing (NLP) systems can prove extremely difficult. In many cases, the best one can do is to evaluate these systems indirectly, by looking at the impact …...

Previous research has demonstrated the importance of handling differences be- tween domains such as “newswire” and “biomedicine” when porting NLP systems from one domain to another. In this paper we …...

AbstractSemantic lexicons and lexical ontologies are some major resources in natural language processing. Developing such resources are time consuming tasks for which some automatic methods are proposed. This paper describes …...

AbstractQuestion answering systems are complex systems using natural language processing. Some evaluation campaigns are organized to evaluate such systems in order to propose a classification of systems based on final …...

AbstractFreeLing is an open-source multilingual language processing library providing a wide range of language analyzers for several languages. It offers text processing and language annotation facilities to natural language processing …...

Verb classes which integrate a wide range of linguistic properties (Levin, 1993) have proved useful for natural language pro- cessing (NLP) applications. However, the real-world use of these classes has …...

AbstractThe paper presents an innovative approach to extract Slovene definition candidates from domain-specific corpora using morphosyntactic patterns, automatic terminology recognition and semantic tagging with wordnet senses. First, a classification model …...

AbstractThe increasing amount of available textual information makes necessary the use of Natural Language Processing (NLP) tools. These tools have to be used on large collections of documents in different …...

AbstractThe need for ontologies has increased in computer science or information science recently. Especially, NLP systems such as information retrieval, machine translation, etc. require ontologies whose concepts are connected to …...

AbstractThis paper presents a geometric approach to meaning representation within the framework of continuous mathematics. Meaning representation is a central issue in Natural Language Processing, in particular for tasks like …...

AbstractAvailability of labeled language resources, such as annotated corpora and domain dependent labeled language resources is crucial for experiments in the field of Natural Language Processing. Most often, due to …...

This paper proposes a method that extracts paraphrases from search engine query logs. The method first extracts paraphrase query-title pairs based on an assumption that a search query and its …...

AbstractThis paper presents the PolNet-Polish WordNet project which aims at building a linguistically oriented ontology for Polish compatible with other WordNet projects such as Princeton WordNet, EuroWordNet and other similarly …...

AbstractSynonyms dictionaries are useful resources for natural language processing. Unfortunately their availability in digital format is limited, as publishing companies do not release their dictionaries in open digital formats. Dicionário-Aberto …...

AbstractCurrently, a great effort is being carried out in the digitalisation of large historical document collections for preservation purposes. The documents in these collections are usually written in ancient languages, …...

AbstractMany NLP applications need fundamental tools to convert the input text into appropriate form or format and extract the primary linguistic knowledge of words and sentences. These tools perform segmentation …...

AbstractNatural language understanding systems require a knowledge base provided with conceptual representations reflecting the structure of human beings’ cognitive system. Although surface semantics can be sufficient in some other systems, …...

AbstractInvestigating differences in linguistic usage between individuals who have suffered brain injury (hereafter patients) and those who haven’t can yield a number of benefits. It provides a better understanding about …...

AbstractWe present the Database of Catalan Adjectives (DCA), a database with 2,296 adjective lemmata enriched with morphological, syntactic and semantic information. This set of adjectives has been collected from a …...

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 …...

AbstractWeb services are increasingly being used in the natural language processing community as a way to increase the interoperability amongst language resources. This paper extends our previous work on integrating …...

AbstractThis paper examines how Natural Language Process (NLP) resources and online dialogue corpora can be used to extend coverage of Information Extraction (IE) templates in a Spoken Dialogue system. IE …...

AbstractIn Natural Language Processing (NLP), the quality of a system depends to a great extent on the quality of the linguistic resources it uses. One area where precise information is …...

Sequence labeling models like conditional random fields have been successfully applied in a variety of NLP tasks. However, as the size of label set and dataset grows, the learning speed …...

Semantic query sub-network is the representation of a natural language query as a graph of semantically connected words. Such sub-networks can be identified as sub-graphs in larger ontologies like DBpedia …...

Started from the very beginning, Stemming has been playing significant roles in several Natural Language Processing Applications such as information retrieval (IR), machine translation (MT), morph analysis and deciding the …...

We investigate differences in point of view (POV) between two objective documents, where one is describing the subject matter in a more positive/negative way than the other, and present an …...

In this paper, we design a processing flow to produce linked data in articles, providing anchor- based term’s additional information and related terms in different languages (English to Chi- nese). …...

_______________________________________________________________________________________________ Do you hear my attitudes? Do you hear my attitudes? Do you hear my attitudes? Do you hear my attitudes? PPPPerception of Mandarin Chinese social affects erception of Mandarin …...

A problem that crops up repeatedly in shallow and deep syntactic parsing approaches to South Asian languages like Urdu/Hindi is the proper treatment of complex predications. Problems for the NLP …...

Distributed representations of words have proven extremely useful in numerous natural lan- guage processing tasks. Their appeal is that they can help alleviate data sparsity problems common to supervised learning. …...

Manual corpus annotation is getting widely used in Natural Language Processing (NLP). While being recognized as a difficult task, no in-depth analysis of its complexity has been performed yet. We …...

Subjectivity analysis has received increasing attention in natural language processing field. Most of the subjectivity analysis works however are conducted on single languages. In this paper, we propose to perform …...

In the present work we raise the hypothesis that eye-movements when reading texts reveal task performance, as measured by the level of understanding of the reader. With the objective of …...

Recently, various unsupervised representation learning approaches have been investigated to produce augmenting features for natural language processing systems in the open-domain learning scenarios. In this paper, we propose a dynamic …...

This paper presents an identification framework for extracting Tibetan base noun phrase (NP). The framework includes two phases. In the first phase, Chinese base NPs are extracted from all Chinese …...

Information structure, i.e the way speakers construct sentences to present new information in the context of old, can capture rich linguistic information about the discourse structure of scientific documents. Information …...

. Using distributional analysis methods to compute semantic proximity links between words has become commonplace in NLP. This paper focuses on the issues of evaluating a dis- tributional resource. We …...

Grouping of terms based on linguistic and semantic regularities in a cross-lingual context We propose to exploit the Natural Language Processing methods dedicated to terminology structuring independently in two languages …...

. This paper presents a set of experiments aiming to (1) assess the contribution of NLP to the specific issue of the readability of texts for French as a foreign …...

Distributed word representations have been widely used and proven to be useful in quite a few natural language processing and text mining tasks. Most of existing word embedding models aim …...

Natural languages (NL) can be classified as prepositional or postpositional based on the order of the noun phrase and the adposition. Categorizing a language by its adposition typology helps in …...

“Deep-syntactic” dependency structures that capture the argumentative, attributive and coordi- native relations between full words of a sentence have a great potential for a number of NLP- applications. The abstraction …...

We consider the problem of automatically paraphrasing a text in order to find an equivalent text that contains a given acrostic. A text contains an acrostic, if the first letters …...

. We present a medical question answering approach, called MEANS. This approach relies on natural language processing techniques to extract medical entities and relations from the user questions and medical …...

Classifying nouns into semantic categories (e.g., animals, food) is an important line of research in both cognitive science and natural language processing. We present a minimally supervised model for noun …...

In the context of Social Media Analytics, Natural Language Processing tools face new chal- lenges on on-line conversational text, such as microblogs, chat, or text messages, because of the specificity …...

This paper proposes an architecture for an open-domain conversational system and evaluates an implemented system. The proposed architecture is fully composed of modules based on natu- ral language processing techniques. …...

Uncertainty detection is essential for many NLP applications. For instance, in information re- trieval, it is of primary importance to distinguish among factual, negated and uncertain informa- tion. Current research …...

In this paper, we explore the relationship between the human-encoded semantics of ontologies and their application to natural language processing (NLP) tasks, such as word-sense disambig- uation (WSD), for which …...

. This article presents the work of Gabriel G. Bès, with extends from the 60s to the 2000s, from structuralism to industrial language analysis. The work of Gabriel G. Bès …...

. One of the recurring questions in natural language processing is the models’s ability to account for the reality of language ability. Chomsky’s Generative Theory and Minimalism are interested in …...

AbstractIn order to apply computational linguistic analyses and pass information to downstream applications, transcriptions of speech obtained via automatic speech recognition (ASR) need to be divided into smaller meaningful units, …...

Given the limited success of medication in re- versing the effects of Alzheimer’s and other dementias, a lot of the neuroscience research has been focused on early detection, in order …...

AbstractWe present a novel way for designing complex joint inference and learning models using Saul (Kordjamshidi et al., 2015), a recently-introduced declarative learning-based programming language (DeLBP). We enrich Saul with …...

This paper presents the challenges and the methodology followed in the design of a new Input Method (IME) for the Taiwanese (Hokkien) language. We first describe the context, the motivations …...

AbstractKnowledge base (KB) such as Freebase plays an important role for many natural language processing tasks. English knowledge base is obviously larger and of higher quality than low resource language …...

AbstractWordNet is probably the best known lexical resource in Natural Language Processing. While it is widely regarded as a high quality repository of concepts and semantic relations, updating and extending …...

AbstractThere are growing needs for patent analysis using Natural Language Processing (NLP)-based approaches. Although NLP-based approaches can extract various information from patents, there are very few approaches proposed to extract …...

AbstractNeural network training has been shown to be advantageous in many natural language processing applications, such as language modelling or machine translation. In this paper, we describe in detail a …...

AbstractNatural-language processing of historical documents is complicated by the abundance of variant spellings and lack of annotated data. A common approach is to normalize the spelling of historical words to …...

AbstractKeystroke dynamics have been extensively used in psycholinguistic and writing research to gain insights into cognitive processing. But do keystroke logs contain actual signal that can be used to learn …...

AbstractRepresenting structured events as vectors in continuous space offers a new way for defining dense features for natural language processing (NLP) applications. Prior work has proposed effective methods to learn …...

AbstractIdentification of Multi-Word Expressions (MWEs) lies at the heart of many natural language processing applications. In this research, we deal with a particular type of Hebrew MWEs, Verb-Noun MWEs (VN-MWEs), …...

AbstractDetermining the intended sense of words in text – word sense disambiguation (WSD) – is a long-standing problem in natural language processing. Recently, researchers have shown promising results using word …...

AbstractCompound words with unmarked word boundaries are problematic for many tasks in NLP and computational linguistics, including information extraction, machine translation, and syllabification. This paper introduces a simple, proof-of-concept language …...

AbstractSemantic Textual Similarity (STS) is a foundational NLP task and can be used in a wide range of tasks. To determine the STS of two texts, hundreds of different STS …...

AbstractRecurrent Neural Network (RNN) is one of the most popular architectures used in Natural Language Processsing (NLP) tasks because its recurrent structure is very suitable to process variable-length text. RNN …...

. VerbNet is a lexical resource for English verbs that has proven useful for NLP thanks to its high lexical and syntactic coverage and its systematic coding of thematic roles. …...

AbstractWe provide a detailed overview of the various approaches that were proposed to date to solve the task of Open Information Extraction. We present the major challenges that such systems …...

AbstractRepresentation learning is a key issue for most Natural Language Processing (NLP) tasks. Most existing representation models either learn little structure information or just rely on pre-defined structures, leading to …...

AbstractRelation Classification aims to classify the semantic relationship between two marked entities in a given sentence. It plays a vital role in a variety of natural language processing applications. Most …...

AbstractSyntactic parsing plays a crucial role in improving the quality of natural language processing tasks. Although there have been several research projects on syntactic parsing in Vietnamese, the parsing quality …...

AbstractThe recently increased focus on misinformation has stimulated research in fact checking, the task of assessing the truthfulness of a claim. Research in automating this task has been conducted in …...

AbstractIn this position paper, we argue that building operational automated scoring systems is a task that has disciplinary complexity above and beyond standard competitive shared tasks which usually involve applying …...

AbstractCommonsense, real-world knowledge about the events that entities or “things in the world” are typically involved in, as well as part-whole relationships, is valuable for allowing computational systems to draw …...

AbstractLong short-term memory (LSTM) language model (LM) has been widely investigated for automatic speech recognition (ASR) and natural language processing (NLP). Although excellent performance is obtained for large vocabulary tasks, …...

AbstractLack of repeatability and generalisability are two significant threats to continuing scientific development in Natural Language Processing. Language models and learning methods are so complex that scientific conference papers no …...

AbstractThe Business Process Management (BPM) field focuses in the coordination of labor so that organizational processes are smoothly executed in a way that products and services are properly delivered. At …...

AbstractIndigenous languages of the American continent are highly diverse. However, they have received little attention from the technological perspective. In this paper, we review the research, the digital resources and …...

AbstractRecent years have witnessed a surge of publications aimed at tracing temporal changes in lexical semantics using distributional methods, particularly prediction-based word embedding models. However, this vein of research lacks …...

AbstractHumor recognition is an interesting and challenging task in natural language processing. This paper proposes to exploit syntactic structure features to enhance humor recognition. Our method achieves significant improvements compared …...

AbstractIn this article, we discuss which text, speech, and image technologies have been developed, and would be feasible to develop, for the approximately 60 Indigenous languages spoken in Canada. In …...

AbstractA paraphrase is a restatement of the meaning of a text in other words. Paraphrases have been studied to enhance the performance of many natural language processing tasks. In this …...

AbstractNeural network based multi-task learning has achieved great success on many NLP problems, which focuses on sharing knowledge among tasks by linking some layers to enhance the performance. However, most …...

AbstractDistributed word embeddings have shown superior performances in numerous Natural Language Processing (NLP) tasks. However, their performances vary significantly across different tasks, implying that the word embeddings learnt by those …...

. Structured prediction lies at the heart of modern Natural language Processing (NLP). In this paper, we study a specific family of structured learning algorithms, loosely refered to as “Learning-to-search” …...

AbstractThe use of machine learning for NLP generally requires resources for training. Tasks performed in a low-resource language usually rely on labeled data in another, typically resource-rich, language. However, there …...

The goal of this paper is to propose measures of innovation through the study of publications in the field of speech and language processing. It is based on the NLP4NLP …...

AbstractQuestion Answering over Knowledge Bases (KB-QA), which automatically answer natural language questions based on the facts contained by a knowledge base, is one of the most important natural language processing …...

In many areas of academic publishing, there is an explosion of literature, and sub-division of fields into subfields, leading to stove-piping where sub-communities of expertise become disconnected from each other. …...

AbstractMulti-label classification is an important yet challenging task in natural language processing. It is more complex than single-label classification in that the labels tend to be correlated. Existing methods tend …...

AbstractRecurrent neural networks have achieved great success in many NLP tasks. However, they have difficulty in parallelization because of the recurrent structure, so it takes much time to train RNNs. …...

AbstractDistributed word representation plays a pivotal role in various natural language processing tasks. In spite of its success, most existing methods only consider contextual information, which is suboptimal when used …...

AbstractK-fold cross validation (CV) is a popular method for estimating the true performance of machine learning models, allowing model selection and parameter tuning. However, the very process of CV requires …...

This paper introduces Weather Voice System applied to information commands-responses system through Public Switched Telephone Network (PSTN). Being able to understand users' voice commands, our system would assist them during …...

This platform includes two main concepts. One, provide multifaceted and deep evaluation for users of Natural Language Processing (NLP) System (such as Syntaxnet or CoreNLP), to help them choose the …...

AbstractCountering online hate speech is a critical yet challenging task, but one which can be aided by the use of Natural Language Processing (NLP) techniques. Previous research has primarily focused …...

AbstractBased on massive amounts of data, recent pretrained contextual representation models have made significant strides in advancing a number of different English NLP tasks. However, for other languages, relevant training …...

AbstractText generation is among the most fundamental tasks in natural language processing. In this paper, we propose a text generation model that learns semantics and structural features simultaneously. This model …...

AbstractGovernmental institutions are employing artificial intelligence techniques to deal with their specific problems and exploit their huge amounts of both structured and unstructured information. In particular, natural language processing and …...

AbstractThis paper presents an approach for prediction of results for sport events. Usually the sport forecasting approaches are based on structured data. We test the hypothesis that the sports results …...

AbstractSymptom diagnosis is a challenging yet profound problem in natural language processing. Most previous research focus on investigating the standard electronic medical records for symptom diagnosis, while the dialogues between …...

AbstractFormality text style transfer plays an important role in various NLP applications, such as non-native speaker assistants and child education. Early studies normalize informal sentences with rules, before statistical and …...

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, …...

AbstractIncorporating Item Response Theory (IRT) into NLP tasks can provide valuable information about model performance and behavior. Traditionally, IRT models are learned using human response pattern (RP) data, presenting a …...

AbstractNatural language has recently been explored as a new medium of supervision for training machine learning models. Here, we explore learning classification tasks using language in a conversational setting – …...

AbstractAutomatically verifying rumorous information has become an important and challenging task in natural language processing and social media analytics. Previous studies reveal that people’s stances towards rumorous messages can provide …...

AbstractRetrieval of relevant vectors produced by representation learning critically influences the efficiency in natural language processing (NLP) tasks. In this paper, we demonstrate an efficient method for searching vectors via …...

AbstractText classification has been one of the major problems in natural language processing. With the advent of deep learning, convolutional neural network (CNN) has been a popular solution to this …...

AbstractRhetorical Structure Theory (RST) parsing is crucial for many downstream NLP tasks that require a discourse structure for a text. Most of the previous RST parsers have been based on …...

AbstractHumor plays important role in human communication, which makes it important problem for natural language processing. Prior work on the analysis of humor focuses on whether text is humorous or …...

AbstractWe present a text-based framework for investigating moral sentiment change of the public via longitudinal corpora. Our framework is based on the premise that language use can inform people’s moral …...

AbstractIn transductive learning, an unlabeled test set is used for model training. Although this setting deviates from the common assumption of a completely unseen test set, it is applicable in …...

AbstractContextualized word embeddings such as ELMo and BERT provide a foundation for strong performance across a wide range of natural language processing tasks by pretraining on large corpora of unlabeled …...

AbstractParaphrase generation (PG) is of great importance to many downstream tasks in natural language processing. Diversity is an essential nature to PG for enhancing generalization capability and robustness of downstream …...

AbstractGenerating knowledge from natural language data has aided in solving many artificial intelligence problems. Vector representations of words have been the driving force behind the majority of natural language processing …...

AbstractCausality represents the most important kind of correlation between events. Extracting causali-ty from text has become a promising hot topic in NLP. However, there is no mature research systems and …...

AbstractOwing to the continuous efforts by the Chinese NLP community, more and more Chinese machine reading comprehension datasets become available. To add diversity in this area, in this paper, we …...

AbstractPersonality profiling has long been used in psychology to predict life outcomes. Recently, automatic detection of personality traits from written messages has gained significant attention in computational linguistics and natural …...

AbstractRecent years have seen important advances in the quality of state-of-the-art models, but this has come at the expense of models becoming less interpretable. This survey presents an overview of …...

AbstractSyntactic dependency parsing is an important task in natural language processing. Unsupervised dependency parsing aims to learn a dependency parser from sentences that have no annotation of their correct parse …...

AbstractStructured semantic sentence representations such as Abstract Meaning Representations (AMRs) are potentially useful in various NLP tasks. However, the quality of automatic parses can vary greatly and jeopardizes their usefulness. …...

AbstractIn this paper, we evaluate the progress of our field toward solving simple factoid questions over a knowledge base, a practically important problem in natural language interface to database. As …...

AbstractWord embedding models learn semantically rich vector representations of words and are widely used to initialize natural processing language (NLP) models. The popular continuous bag-of-words (CBOW) model of word2vec learns …...

AbstractHuman natural language is mentioned at a specific point in time while human emotions change over time. While much work has established a strong link between language use and emotional …...

AbstractThe meaning of natural language text is supported by cohesion among various kinds of entities, including coreference relations, predicate-argument structures, and bridging anaphora relations. However, predicate-argument structures for nominal predicates …...

AbstractWe introduce Biased TextRank, a graph-based content extraction method inspired by the popular TextRank algorithm that ranks text spans according to their importance for language processing tasks and according to …...

AbstractPopular approaches to natural language processing create word embeddings based on textual co-occurrence patterns, but often ignore embodied, sensory aspects of language. Here, we introduce the Python package comp-syn, which …...

AbstractContinual learning (CL) aims to enable information systems to learn from a continuous data stream across time. However, it is difficult for existing deep learning architectures to learn a new …...

AbstractEmotion prediction is a critical task in the field of Natural Language Processing (NLP). There has been a significant amount of work done in emotion prediction for resource-rich languages. There …...

AbstractAdvances in word representations have shown tremendous improvements in downstream NLP tasks, but lack semantic interpretability. In this paper, we introduce Definition Frames (DF), a matrix distributed representation extracted from …...

AbstractEmotion-cause pair extraction (ECPE), which aims at simultaneously extracting emotion-cause pairs that express emotions and their corresponding causes in a document, plays a vital role in understanding natural languages. Considering …...

AbstractAutomatic analysis of connected speech by natural language processing techniques is a promising direction for diagnosing cognitive impairments. However, some difficulties still remain: the time required for manual narrative transcription …...

AbstractWe explore end-to-end trained differentiable models that integrate natural logic with neural networks, aiming to keep the backbone of natural language reasoning based on the natural logic formalism while introducing …...

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. …...

AbstractThe recent dominance of machine learning-based natural language processing methods has fostered the culture of overemphasizing model accuracies rather than studying the reasons behind their errors. Interpretability, however, is a …...

AbstractRecent work has shown that pre-trained Transformers obtain remarkable performance on many natural language processing tasks including automatic summarization. However, most work has focused on (relatively) data-rich single-document summarization settings. …...

AbstractAdversarial attacks are label-preserving modifications to inputs of machine learning classifiers designed to fool machines but not humans. Natural Language Processing (NLP) has mostly focused on high-level attack scenarios such …...

AbstractMath word problem solving is an emerging research topic in Natural Language Processing. Recently, to address the math word problem-solving task, researchers have applied the encoder-decoder architecture, which is mainly …...

AbstractAcademia and industry have developed machine learning and natural language processing models to detect online hate speech automatically. However, most of these existing methods adopt a supervised approach that heavily …...

AbstractReview rating prediction of text reviews is a rapidly growing technology with a wide range of applications in natural language processing. However, most existing methods either use hand-crafted features or …...

AbstractTopic segmentation is critical in key NLP tasks and recent works favor highly effective neural supervised approaches. However, current neural solutions are arguably limited in how they model context. In …...

AbstractPre-training sentence encoders is effective in many natural language processing tasks including machine translation (MT) quality estimation (QE), due partly to the scarcity of annotated QE data required for supervised …...

AbstractRecent progress in text classification has been focused on high-resource languages such as English and Chinese. For low-resource languages, amongst them most African languages, the lack of well-annotated data and …...

AbstractLarge text corpora are increasingly important for a wide variety of Natural Language Processing (NLP) tasks, and automatic language identification (LangID) is a core technology needed to collect such datasets …...

AbstractDependency trees have been shown to be effective in capturing long-range relations between target entities. Nevertheless, how to selectively emphasize target-relevant information and remove irrelevant content from the tree is …...

AbstractSentiment analysis is a widely researched NLP problem with state-of-the-art solutions capable of attaining human-like accuracies for various languages. However, these methods rely heavily on large amounts of labeled data …...

AbstractCrowdsourcing is used in academia and industry to solve tasks that are easy for humans but hard for computers, in natural language processing mostly to annotate data. The quality of …...

AbstractDeep neural networks excel at learning from labeled data and achieve state-of-the-art results on a wide array of Natural Language Processing tasks. In contrast, learning from unlabeled data, especially under …...

AbstractSemantic embeddings have advanced the state of the art for countless natural language processing tasks, and various extensions to multimodal domains, such as visual-semantic embeddings, have been proposed. While the …...

AbstractThis paper presents the Multilingual Artificial Intelligence Agent Assistant (MAIA), a project led by Unbabel with the collaboration of CMU, INESC-ID and IT Lisbon. MAIA will employ cutting-edge machine learning …...

AbstractEmotion analysis in polarized contexts represents a challenge for Natural Language Processing modeling. As a step in the aforementioned direction, we present a methodology to extend the task of Aspect-based …...

AbstractDeep pre-trained language models tend to become ubiquitous in the field of Natural Language Processing (NLP). These models learn contextualized representations by using a huge amount of unlabeled text data …...

AbstractNatural Language Query interfaces allow the end-users to access the desired information without the need to know any specialized query language, data storage, or schema details. Even with the recent …...

AbstractSentence matching aims to identify the special relationship between two sentences, and plays a key role in many natural language processing tasks. However, previous studies mainly focused on exploiting either …...

AbstractConversational Emotion Recognition (CER) is a crucial task in Natural Language Processing (NLP) with wide applications. Prior works in CER generally focus on modeling emotion influences solely with utterance-level features, …...

The issue of preservation and revitalization of the indigenous languages is gaining attention from the public in recent days. Developing NLP techniques related to the indigenous languages will help to …...

AbstractUnderstanding neural models is a major topic of interest in the deep learning community. In this paper, we propose to interpret a general neural model comparatively. Specifically, we study the …...

AbstractAn NLP model’s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource …...

AbstractSentence complexity assessment is a relatively new task in Natural Language Processing. One of its aims is to highlight in a text which sentences are more complex to support the …...

AbstractWith growing applications of Machine Learning in daily lives Natural Language Processing (NLP) has emerged as a heavily researched area. Finding its applications in tasks ranging from simple Q/A chatbots …...

AbstractDue to the development of deep learning, the natural language processing tasks have made great progresses by leveraging the bidirectional encoder representations from Transformers (BERT). The goal of information retrieval …...

AbstractWith the recent breakthrough of deep learning technologies, research on machine reading comprehension (MRC) has attracted much attention and found its versatile applications in many use cases. MRC is an …...

AbstractTo provide analysis of recent researches of automatic question generation from text,we surveyed 9 papers between 2019 to early 2021, retrieved from Paper with Code(PwC). Our research follows the survey …...

AbstractThe sentence is a fundamental unit of text processing. Yet sentences in the wild are commonly encountered not in isolation, but unsegmented within larger paragraphs and documents. Therefore, the first …...

AbstractThis position paper investigates the problem of automated text anonymisation, which is a prerequisite for secure sharing of documents containing sensitive information about individuals. We summarise the key concepts behind …...

AbstractPre-trained language models (PLMs) have achieved great success in natural language processing. Most of PLMs follow the default setting of architecture hyper-parameters (e.g., the hidden dimension is a quarter of …...

AbstractEvaluation in NLP is usually done by comparing the scores of competing systems independently averaged over a common set of test instances. In this work, we question the use of …...

AbstractNatural Language Processing (NLP) is increasingly relying on general end-to-end systems that need to handle many different linguistic phenomena and nuances. For example, a Natural Language Inference (NLI) system has …...

AbstractSentence embeddings are an important component of many natural language processing (NLP) systems. Like word embeddings, sentence embeddings are typically learned on large text corpora and then transferred to various …...

AbstractAlthough deep neural networks have achieved prominent performance on many NLP tasks, they are vulnerable to adversarial examples. We propose Dirichlet Neighborhood Ensemble (DNE), a randomized method for training a …...

AbstractPre-trained Language Models (PLMs) have shown superior performance on various downstream Natural Language Processing (NLP) tasks. However, conventional pre-training objectives do not explicitly model relational facts in text, which are …...

AbstractLeaderboards are widely used in NLP and push the field forward. While leaderboards are a straightforward ranking of NLP models, this simplicity can mask nuances in evaluation items (examples) and …...

AbstractWhile state-of-the-art NLP models have been achieving the excellent performance of a wide range of tasks in recent years, important questions are being raised about their robustness and their underlying …...

AbstractWe describe an efficient hierarchical method to compute attention in the Transformer architecture. The proposed attention mechanism exploits a matrix structure similar to the Hierarchical Matrix (H-Matrix) developed by the …...

AbstractSigned languages are the primary means of communication for many deaf and hard of hearing individuals. Since signed languages exhibit all the fundamental linguistic properties of natural language, we believe …...

AbstractNatural Language Processing (NLP) systems learn harmful societal biases that cause them to amplify inequality as they are deployed in more and more situations. To guide efforts at debiasing these …...

AbstractMulti-label text classification is one of the fundamental tasks in natural language processing. Previous studies have difficulties to distinguish similar labels well because they learn the same document representations for …...

AbstractThe advent of large pre-trained language models has given rise to rapid progress in the field of Natural Language Processing (NLP). While the performance of these models on standard benchmarks …...

AbstractWe propose to measure fine-grained domain relevance– the degree that a term is relevant to a broad (e.g., computer science) or narrow (e.g., deep learning) domain. Such measurement is crucial …...

AbstractPre-trained language models have been applied to various NLP tasks with considerable performance gains. However, the large model sizes, together with the long inference time, limit the deployment of such …...

AbstractGraph convolutional network (GCN) has become popular in various natural language processing (NLP) tasks with its superiority in long-term and non-consecutive word interactions. However, existing single-hop graph reasoning in GCN …...

AbstractAn overarching goal of natural language processing is to enable machines to communicate seamlessly with humans. However, natural language can be ambiguous or unclear. In cases of uncertainty, humans engage …...

AbstractWhile counterfactual examples are useful for analysis and training of NLP models, current generation methods either rely on manual labor to create very few counterfactuals, or only instantiate limited types …...

AbstractOrganisations disclose their privacy practices by posting privacy policies on their websites. Even though internet users often care about their digital privacy, they usually do not read privacy policies, since …...

AbstractBehavior of deep neural networks can be inconsistent between different versions. Regressions during model update are a common cause of concern that often over-weigh the benefits in accuracy or efficiency …...

AbstractRecent researches have shown that large natural language processing (NLP) models are vulnerable to a kind of security threat called the Backdoor Attack. Backdoor attacked models can achieve good performance …...

AbstractIn selective prediction, a classifier is allowed to abstain from making predictions on low-confidence examples. Though this setting is interesting and important, selective prediction has rarely been examined in natural …...

AbstractNatural language processing (NLP) applications are now more powerful and ubiquitous than ever before. With rapidly developing (neural) models and ever-more available data, current NLP models have access to more …...

AbstractPart-of-Speech (POS) tags are routinely included as features in many NLP tasks. However, the importance and usefulness of POS tags needs to be examined as NLP expands to low-resource languages …...

We describe a new annotated dataset for Low Saxon with the intention to complement ex- isting corpora. This corpus covers the period from the 15th to the 21st century and …...

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 …...

AbstractEvent argument extraction (EAE) aims to extract arguments with given roles from texts, which have been widely studied in natural language processing. Most previous works have achieved good performance in …...

AbstractIrony is a ubiquitous figurative language in daily communication. Previously, many researchers have approached irony from linguistic, cognitive science, and computational aspects. Recently, some progress have been witnessed in automatic …...

AbstractAlthough automatic text summarization (ATS) has been researched for several decades, the application of graph neural networks (GNNs) to this task started relatively recently. In this survey we provide an …...

AbstractAsymmetric text matching has becoming increasingly indispensable for many downstream tasks (e.g., IR and NLP). Here, asymmetry means that the documents involved for matching hold different amounts of information, e.g., …...

AbstractCross-lingual transfer learning has proven useful in a variety of Natural Language (NLP) tasks, but it is understudied in the context of legal NLP, and not at all in Legal …...

AbstractData augmentation techniques have been proven useful in many applications in NLP fields. Most augmentations are task-specific, and cannot be used as a general-purpose tool. In our work, we present …...

AbstractNatural Language Processing (NLP) has become increasingly utilized to provide adaptivity in educational applications. However, recent research has highlighted a variety of biases in pre-trained language models. While existing studies …...

AbstractCode-mixing refers to the mixed use of multiple languages. It is prevalent in multilingual societies and is also one of the most challenging natural language processing tasks. In this paper, …...

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 …...

AbstractNatural Language Processing is increasingly being applied in the finance and business industry to analyse the text of many different types of financial documents. Given the increasing growth of firms …...

AbstractSocial media is the ultimate challenge for many natural language processing tools. The constant emergence of linguistic constructs challenge even the most sophisticated NLP tools. Predicting word embeddings for out …...

Abstract“Learning sentence representation is a fundamental task in natural language processing and has been studied extensively. Recently, many works have obtained high-quality sentence representation based on contrastive learning from pre-trained …...

AbstractScientific literature serves as a high-quality corpus, supporting a lot of Natural Language Processing (NLP) research. However, existing datasets are centered around the English language, which restricts the development of …...

AbstractDeepSPIN is a research project funded by the European Research Council (ERC) whose goal is to develop new neural structured prediction methods, models, and algorithms for improving the quality, interpretability, …...

AbstractDocument-level Event Factuality Identification (DEFI) predicts the factuality of a specific event based on a document from which the event can be derived, which is a fundamental and crucial task …...

AbstractEMBEDDIA project developed a range of resources and methods for less-resourced EU languages, focusing on applications for media industry, including keyword extraction, comment moderation and article generation....

AbstractLarge-scale pretrained language models have led to significant improvements in Natural Language Processing. Unfortunately, they come at the cost of high computational and storage requirements that complicate their deployment on …...

AbstractHierarchical Text Classification (HTC), which aims to predict text labels organized in hierarchical space, is a significant task lacking in investigation in natural language processing. Existing methods usually encode the …...

AbstractIn this work, we propose a Named Entity handling approach to improve translation quality within an existing Natural Language Processing (NLP) pipeline without modifying the Neural Machine Translation (NMT) component. …...

AbstractGraph Neural Network (GNN)-based models have proven effective in various Natural Language Processing (NLP) tasks in recent years. Specifically, in the case of the Extractive Document Summarization (EDS) task, modeling …...

AbstractKeyphrase Prediction (KP) is an established NLP task, aiming to yield representative phrases to summarize the main content of a given document. Despite major progress in recent years, existing works …...

AbstractSifting through hundreds of old case documents to obtain information pertinent to the case in hand has been a major part of the legal profession for centuries. However, with the …...

AbstractThe quality of Natural Language Processing (NLP) models is typically measured by the accuracy or error rate of a predefined test set. Because the evaluation and optimization of these measures …...

AbstractName Entity Recognition (NER) is a very important and basic task in traditional NLP tasks. In the biomedical field, NER tasks have been widely used in various products developed by …...

AbstractIncorporating information from other languages can improve the results of tasks in low-resource languages. A powerful method of building functional natural language processing systems for low-resource languages is to combine …...

AbstractControllable story generation is a challenging task in the field of NLP, which has attracted increasing research interest in recent years. However, most existing works generate a whole story conditioned …...

AbstractRecent research has revealed undesirable biases in NLP data and models. However, these efforts focus of social disparities in West, and are not directly portable to other geo-cultural contexts. In …...

AbstractWe present a corrected version of a subset of the FactBank data set. Previously published results on FactBank are no longer valid. We perform experiments on FactBank using multiple training …...

AbstractText-to-SQL has attracted attention from both the natural language processing and database communities because of its ability to convert the semantics in natural language into SQL queries and its practical …...

AbstractScientific paper summarization is always challenging in Natural Language Processing (NLP) since it is hard to collect summaries from such long and complicated text. We observe that previous works tend …...

AbstractIn embodied cognition, physical experiences are believed to shape abstract cognition, such as natural language and reasoning. Image schemas were introduced as spatio-temporal cognitive building blocks that capture these recurring …...

AbstractNatural language processing (NLP) systems have become a central technology in communication, education, medicine, artificial intelligence, and many other domains of research and development. While the performance of NLP methods …...

Abstract“Traditional Chinese Medicine (TCM) is a natural, safe, and effective therapy that has spread and been applied worldwide. The unique TCM diagnosis and treatment system requires a comprehensive analysis of …...

AbstractWhile pre-trained language models like BERT have achieved impressive results on various natural language processing tasks, deploying them on resource-restricted devices is challenging due to their intensive computational cost and …...

AbstractThe world of pronouns is changing – from a closed word class with few members to an open set of terms to reflect identities. However, Natural Language Processing (NLP) barely …...

AbstractPre-trained transformer-based language models have achieved state-of-the-art performance in many areas of NLP. It is still an open question whether the models are capable of integrating syntax and semantics in …...

In this study, we assess ChatGPT, OpenAI’s latest conversational chatbot and large lan- guage model (LLM), on its performance in elementary-grade arithmetic and logic prob- lems. Despite its impressive coherence …...

Abstract“Event detection (ED) is a crucial area of natural language processing that automates the extrac-tion of specific event types from large-scale text, and studying historical ED in classical Chinesetexts helps …...

In this research, we comprehensively ana- lyze the potential biases inherent in Large Language Model, utilizing meticulously cu- rated input data to ascertain the extent to which such data sway …...

AbstractPre-trained models have drastically changed the field of natural language processing by providing a way to leverage large-scale language representations to various tasks. Some pre-trained models offer general-purpose representations, while …...

Abstract“A simile is an important linguistic phenomenon in daily communication and an important taskin natural language processing (NLP). In recent years, pre-trained language models (PLMs) haveachieved great success in NLP …...

AbstractAs an efficient approach to understand, generate, and process natural language texts, research in natural language processing (NLP) has exhibited a rapid spread and wide adoption in recent years. Given …...

Abstract“Conditional question answering (CQA) is an important task in natural language processing thatinvolves answering questions that depend on specific conditions. CQA is crucial for domainsthat require the provision of personalized …...

AbstractNatural language processing (NLP) has a significant impact on society via technologies such as machine translation and search engines. Despite its success, NLP technology is only widely available for high-resource …...