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With the increasing use of research paper search engines, such as CiteSeer, for both lit- erature search and hiring decisions, the accu- racy of such systems is of paramount impor- …...

We extend previous work on tree kernels to estimate the similarity between the dependency trees of sen- tences. Using this kernel within a Support Vector Machine, we detect and classify …...

We present an automatic approach to tree annota- tion in which basic nonterminal symbols are alter- nately split and merged to maximize the likelihood of a training treebank. Starting with …...

We propose a general method for reranker construction which targets choosing the candidate with the least expected loss, rather than the most probable candidate. Different approaches to expected loss ap- …...

We present a corpus-based supervised lear- ning system for coarse-grained sense disam- biguation. In addition to usual features for training in word sense disambiguation, our system also uses Base Level …...

We describe and evaluate a new method of automatic seed word selection for un- supervised sentiment classification of product reviews in Chinese. The whole method is unsupervised and does not …...

AbstractIn this paper we present the Cast3LB-CoNLL-SemRol corpus, currently the only corpus of Spanish annotated with dependency syntax and semantic roles, and the tools that have been trained on the …...

We show that jointly parsing a bitext can sub- stantially improve parse quality on both sides. In a maximum entropy bitext parsing model, we define a distribution over source trees, …...

We analyze some of the fundamental design challenges and misconceptions that underlie the development of an efficient and robust NER system. In particular, we address issues such as the representation …...

Seed sampling is critical in semi-supervised learning. This paper proposes a clustering- based stratified seed sampling approach to semi-supervised learning. First, various clus- tering algorithms are explored to partition the …...

AbstractIn this paper, we describe our hybrid parsing model on the Mandarin Chinese processing. In particular, we work on the Tsinghua Chinese Treebank (TCT), whose annotation has both constitutes and …...

AbstractIn this paper, we present the results of an experiment with utilizing a stochastic morphosyntactic tagger as a pre-processing module of a rule-based chunker and partial parser for Croatian in …...

AbstractThis paper compares several indexing methods for person names extracted from text, developed for an information retrieval system with requirements for fast approximate matching of noisy and multicultural Romanized names. …...

Knowledge extraction from online reposito- ries such as PubMed holds the promise of dramatically speeding up biomedical research and drug design. After initially focusing on recognizing proteins and binary interactions, …...

Tree-based translation models, which ex- ploit the linguistic syntax of source lan- guage, usually separate decoding into two steps: parsing and translation. Although this separation makes tree-based decoding simple and …...

Many researchers are trying to use information extraction (IE) to create large-scale knowl- edge bases from natural language text on the Web. However, the primary approach (su- pervised learning of …...

AbstractWe investigate a number of approaches to generating Stanford Dependencies, a widely used semantically-oriented dependency representation. We examine algorithms specifically designed for dependency parsing (Nivre, Nivre Eager, Covington, Eisner, and …...

This paper analyzes the contribution of se- mantic roles to TimeML event recognition and classification. For that purpose, an approach using conditional random fields with a variety of morphosyntactic features …...

AbstractThe paper presents results of an experiment dealing with sentiment analysis of Croatian text from the domain of finance. The goal of the experiment was to design a system model …...

AbstractA novel method to automatically associate ontological concepts to their realisations in texts is presented. The method has been developed in the context of the Papyrus project to annotate texts …...

We report on empirical results in extreme extraction. It is extreme in that (1) from re- ceipt of the ontology specifying the target concepts and relations, development is li- mited …...

Multiword expressions (MWE), a known nui- sance for both linguistics and NLP, blur the lines between syntax and semantics. Previous work on MWE identification has relied primar- ily on surface …...

People tweet more than 100 Million times daily, yielding a noisy, informal, but some- times informative corpus of 140-character messages that mirrors the zeitgeist in an un- precedented manner. The …...

In this paper we present a method for unsuper- vised semantic role induction which we for- malize as a graph partitioning problem. Ar- gument instances of a verb are represented …...

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

This paper presents a mixed deterministic model for coreference resolution in the CoNLL-2012 shared task. We separate the two main stages of our model, mention detection and coreference resolution, into …...

Generating coherent discourse is an important aspect in natural language generation. Our aim is to learn factors that constitute coherent discourse from data, with a focus on how to re- …...

This paper presents a novel approach to document-based discourse analysis by performing a global A* search over the space of possible structures while optimizing a global criterion over the set …...

Automatic detection of negation cues along with their scope and corresponding negated events is an important task that could bene- fit other natural language processing (NLP) tasks such as extraction …...

This paper describes a coreference resolution system for CONLL 2012 shared task developed by HLT_HITSZ group, which incorporates rule-based and statistic-based techniques. The system performs coreference resolution through the mention …...

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

We present work on linking events and flu- ents (i.e., relations that hold for certain periods of time) to temporal information in text, which is an important enabler for many …...

This paper contributes an approach for expressing non-concatenative morphological phenomena, such as stem derivation Semitic languages, in terms of a mildlyin context-sensitive grammar formalism. This offers a convenient level of …...

Our goal is to extract answers from pre- retrieved sentences for Question Answering (QA). We construct a linear-chain Conditional Random Field based on pairs of questions and their possible answer …...

In this paper, we analyze a novel set of fea- tures for the task of automatic edit category classification. Edit category classification as- signs categories such as spelling error correc- …...

We propose the hypothesis that word ety- mology is useful for NLP applications as a bridge between languages. We support this hypothesis with experiments in cross- language (English-Italian) document cat- …...

In this paper we classify the temporal relations between pairs of events on an article-wide ba- sis. This is in contrast to much of the exist- ing literature which focuses …...

This paper proposes a new method for significantly improving the performance of pairwise coreference models. Given a set of indicators, our method learns how to best separate types of mention …...

Learning a hierarchy of specialized pairwise models for coreference resolution This paper proposes a new method for significantly improving the performance of pairwise coreference models. Given a set of indicators, …...

Automatic relation extraction provides great support for scientists and database curators in dealing with the extensive amount of biomed- ical textual data. The DDIExtraction 2013 challenge poses the task of …...

Information extraction from microblog posts is an important task, as today microblogs cap- ture an unprecedented amount of information and provide a view into the pulse of the world. As …...

Microblogging services have attracted hundreds of millions of users to publish their status, ideas and thoughts, everyday. These microblog posts have also become one of the most attractive and valuable …...

This paper presents a first version of LinkPeople, an entity-centric system for coreference reso- lution of person entities. The approach combines (i) a multi-pass architecture which takes advan- tage of …...

This paper reports on the development of a hy- brid and simple method based on a machine learning classifier (Naive Bayes), Word Sense Disambiguation and rules, for the automatic assignment …...

AbstractWe present a simple, easy-to-replicate monolingual aligner that demonstrates state-of-the-art performance while relying on almost no supervision and a very small number of external resources. Based on the hypothesis that …...

AbstractIn this paper we present a bilingual transliteration lexicon of 170K Japanese-English technical terms in the scientific domain. Translation pairs are extracted by filtering a large list of transliteration candidates …...

We investigate grammatical error detec- tion in spoken language, and present a data-driven method to train a dependency parser to automatically identify and label grammatical errors. This method is ag- …...

Although semi-supervised model can extract the event mentions matching frequent event patterns, it suf- fers much from those event mentions, which match infrequent patterns or have no matching pattern. To …...

In this paper we present our task-based evaluation of query biased summarization for cross-language information retrieval (CLIR) using relevance prediction. We de- scribe our 13 summarization methods each from one …...

AbstractIn news stories, event mentions denote real-world events of different spatial and temporal granularity. Narratives in news stories typically describe some real-world event of coarse spatial and temporal granularity along …...

We propose three improvements to ad- dress the drawbacks of state-of-the-art transition-based constituent parsers. First, to resolve the error propagation problem of the traditional pipeline approach, we incorporate POS tagging …...

In this paper, we describe how we cre- ated a meta-classifier to detect the mes- sage-level sentiment of tweets. We par- ticipated in SemEval-2014 Task 9B by combining the results …...

We explore the extent to which high- resource manual annotations such as tree- banks are necessary for the task of se- mantic role labeling (SRL). We examine how performance changes …...

Several state-of-the-art event extraction sys- tems employ models based on Support Vec- tor Machines (SVMs) in a pipeline architec- ture, which fails to exploit the joint depen- dencies that typically …...

We describe the submission of the SAP Research & Innovation team to the Se- mEval 2014 Task 9: Sentiment Analy- sis in Twitter. We challenged ourselves to develop a competitive …...

We describe the submission of the team of the Sofia University to SemEval-2014 Task 9 on Sentiment Analysis in Twit- ter. We participated in subtask B, where the participating systems …...

This paper describes the system that has been used by TeamX in SemEval-2014 Task 9 Subtask B. The system is a senti- ment analyzer based on a supervised text categorization …...

The task of recommending hashtags for microblogs has been received considerable attention in recent years, and many applications can reap enormous benefits from it. Various approaches have been proposed to …...

Derivationally related lemmas like friendN – friendlyA – friendshipN are derived from a common stem. Frequently, their meanings are also systematically related. However, there are also many examples of derivationally …...

Code-switching is the practice of moving back and forth between two languages in spoken or written form of communication. In this paper, we address the problem of word-level language identification …...

We explore using recursive autoencoders for SemEval 2015 Task 1: Paraphrase and Seman- tic Similarity in Twitter. Our paraphrase de- tection system makes use of phrase-structure parse tree embeddings that …...

We implemented a syntactic and semantic tagging system for SemEval 2015 Task 15: Corpus Pattern Analysis. For syntactic tagging, we present a Dependency Chain Search Algorithm that is found to …...

We describe a topic model based approach for selectional preference. Using the topic fea- tures generated by an LDA model on the ex- tracted predicate-arguments over the Chinese Gigaword corpus, …...

In this paper we describe an approach to automatic cognate identification in mono- lingual texts using machine translation. This system was used as our entry in the 2015 ALTA shared …...

toIn this paper we present the first ever, the best of our knowledge, dis- course parser for multi-party chat dia- logues. Discourse in multi-party dia- logues dramatically differs from mono- …...

Bootstrapped classifiers iteratively generalize from a few seed examples or prototypes to other examples of target labels. However, sparseness of language and limited supervi- sion make the task difficult. We …...

We explore the impact of adding distri- butional knowledge to a state-of-the-art coreference resolution system. By inte- grating features based on word and context expansions from a distributional thesaurus (DT), …...

We describe the system we developed to partic- ipate in SemEval 2015 Task 1, Paraphrase and Semantic Similarity in Twitter. We create simi- larity vectors from two-skip trigrams of prepro- …...

Monolingual alignment is the task of pair- ing semantically similar units from two pieces of text. We report a top-performing supervised aligner that operates on short text snippets. We employ …...

With the increasing automation of health care information processing, it has become crucial to extract meaningful information from textual notes in electronic medical records. One of the key challenges is …...

Scarcity of annotated corpora for many languages is a bottleneck for training fine- grained sentiment analysis models that can tag aspects and subjective phrases. We pro- pose to exploit statistical …...

We present labeled morphological segmentation—an alternative view of morphological processing that unifies sev- eral tasks. We introduce a new hierarchy of morphotactic tagsets and CHIPMUNK, a discriminative morphological segmen- tation …...

This paper describes our sentiment analysis systems which have been built for SemEval- 2015 Task 10 Subtask B and E. For sub- task B, a Logistic Regression classifier has been …...

We describe a novel max-margin learn- ing approach to optimize non-linear perfor- mance measures for distantly-supervised re- lation extraction models. Our approach can be generally used to learn latent variable …...

This paper describes the system submit- ted by the University of Heidelberg to the Shared Task on Word-level Quality Esti- mation at the 2015 Workshop on Statis- tical Machine Translation. …...

Response-based learning allows to adapt a sta- tistical machine translation (SMT) system to an extrinsic task by extracting supervision sig- nals from task-specific feedback. In this pa- per, we elicit …...

Paraphrase recognition is the task of iden- tifying whether two pieces of natural lan- guage represent similar meanings. This pa- per describes a system participating in the shared task 1 …...

Sarcasm is generally characterized as a figure of speech that involves the substi- tution of a literal by a figurative mean- ing, which is usually the opposite of the original …...

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

We use character-based statistical machine translation in order to correct user search queries in the e-commerce domain. The training data is automatically extracted from event logs where users re-issue their …...

This paper describes SWATAC, a system built for SemEval-2015’s Task 10 Subtask B, namely the Message Polarity Classification Task. Given a tweet, the system classifies the sentiment as either positive, …...

This paper presents the SWATCS65 ensem- ble classifier used to identify the sentiment of tweets. The classifier was trained and tested using data provided by Semeval-2015, Task 10, subtask B …...

Medical texts are filled with mentions of dis- eases, disorders, and other clinical conditions, with many different surface forms relating to the same condition. We describe MINERAL, a system for …...

When tweeting on a topic, Twitter users often post messages that convey the same or similar meaning. We describe TweetingJay, a system for detecting paraphrases and semantic simi- larity of …...

We present two approaches to time expres- sion identification, as entered in to SemEval- 2015 Task 6, Clinical TempEval. The first is a comprehensive rule-based approach that favoured recall, and …...

Speech transcripts often only capture seman- tic content, omitting disfluencies that can be useful for analyzing social dynamics of a dis- cussion. This work describes steps in build- ing a …...

AbstractCyberbullying statistics are shocking, the number of affected young people is increasing dramatically with the affordability of mobile technology devices combined with a growing number of social networks. This paper …...

AbstractReaders usually rely on abstracts to identify relevant medical information from scientific articles. Abstracts are also essential to advanced information retrieval methods. More than 50 thousand scientific publications in PubMed …...

AbstractWe report on the creation of a lexical resource for the identification of potentially unspecific or imprecise constructions in German requirements documentation from the car manufacturing industry. In requirements engineering, …...

AbstractExisting systems deliver high accuracy and F1-scores for detecting paraphrase and semantic similarity on traditional clean-text corpus. For instance, on the clean-text Microsoft Paraphrase benchmark database, the existing systems attain …...

AbstractIn this paper, we present a study on tweet classification which aims to define the communication behavior of the 103 French museums that participated in 2014 in the Twitter operation: …...

For AI systems to reason about real world situ- ations, they need to recognize which processes are at play and which entities play key roles in them. Our goal is …...

AbstractIn this paper, we investigate four important issues together for explicit discourse relation labelling in Chinese texts: (1) discourse connective extraction, (2) linking ambiguity resolution, (3) relation type disambiguation, and …...

This paper describes system details and results of team “EOF” from the Univer- sity of Melbourne in the shared task of ALTA 2016, which addresses the use of cross document …...

In this paper we describe our system (DTSim) submitted at SemEval-2016 Task 2: Inter- pretable Semantic Textual Similarity (iSTS). We participated in both gold chunks cate- gory (texts chunked by …...

We present a novel end-to-end neural model to extract entities and relations be- tween them. Our recurrent neural net- work based model captures both word se- quence and dependency tree …...

AbstractOn microblogging services, people usually use hashtags to mark microblogs, which have a specific theme or content, making them easier for users to find. Hence, how to automatically recommend hashtags …...

AbstractMicroblogging services allow users to create hashtags to categorize their posts. In recent years, the task of recommending hashtags for microblogs has been given increasing attention. However, most of existing …...

AbstractTwitter-related studies often need to geo-locate Tweets or Twitter users, identifying their real-world geographic locations. As tweet-level geotagging remains rare, most prior work exploited tweet content, timezone and network information …...

This paper demonstrates that it is possible for a parser to improve its performance with a hu- man in the loop, by posing simple questions to non-experts. For example, given …...

AbstractNowadays, neural networks play an important role in the task of relation classification. By designing different neural architectures, researchers have improved the performance to a large extent in comparison with …...

In this work, we show the possibility of infer- ring the answer type before solving a factoid question and leveraging the type information to improve semantic parsing. By replacing the …...

AbstractIdentifying dialogue acts and dialogue modes during tutorial interactions is an extremely crucial sub-step in understanding patterns of effective tutor-tutee interactions. In this work, we develop a novel joint inference …...

AbstractSemantic role labeling (SRL) is the task of identifying and labeling predicate-argument structures in sentences with semantic frame and role labels. A known challenge in SRL is the large number …...

Recurrent Neural Network (RNN) and one of its specific architectures, Long Short-Term Memory (LSTM), have been widely used for sequence labeling. Explicitly modeling out- put label dependencies on top of …...

We describe MITRE’s submission to the SemEval-2016 Task 6, Detecting Stance in Tweets. This effort achieved the top score in Task A on supervised stance detection, pro- ducing an average …...

AbstractRecognising and understanding irony is crucial for the improvement natural language processing tasks including sentiment analysis. In this study, we describe the construction of an English Twitter corpus and its …...

Automatic enrichment of semantic tax- onomies with novel data is a relatively unexplored task with potential benefits in a broad array of natural language processing problems. Task 14 of SemEval …...

AbstractROOT9 is a supervised system for the classification of hypernyms, co-hyponyms and random words that is derived from the already introduced ROOT13 (Santus et al., 2016). It relies on a …...

Semantic error detection and correction is an important task for applications such as fact checking, speech-to-text or grammatical er- ror correction. Current approaches gener- ally focus on relatively shallow semantics …...

For the Australasian Language Technology Association (ALTA) 2016 Shared Task, we devised Pairwise FastText Classifier (PFC), an efficient embedding-based text classifier, and used it for entity disambiguation. Com- pared with …...

AbstractRelation classification is the task of classifying the semantic relations between entity pairs in text. Observing that existing work has not fully explored using different representations for relation instances, especially …...

We propose a new open question answer- ing framework for question answering over a knowledge base (KB). Our system uses both a curated KB, Freebase, and one that is ex- …...

AbstractBar exams provide a key watershed by which legal professionals demonstrate their knowledge of the law and its application. Passing the bar entitles one to practice the law in a …...

Existing knowledge-based question an- swering systems often rely on small an- notated training data. While shallow meth- ods like relation extraction are robust to data scarcity, they are less expressive …...

We propose recurrent support vector machine (RSVM) for slot tagging. This model is a combi- nation of the recurrent neural network (RNN) and the structured support vector machine. RNN extracts …...

AbstractWe extend classic review mining work by building a binary classifier that predicts whether a review of a documentary film was written by an expert or a layman with 90.70% …...

This task combines the labeling of multiword expressions and supersenses (coarse-grained classes) in an explicit, yet broad-coverage paradigm for lexical semantics. Nine sys- tems participated; the best scored 57.7% F1 …...

In this report we summarize the results of the SemEval 2016 Task 8: Meaning Representa- tion Parsing. Participants were asked to gener- ate Abstract Meaning Representation (AMR) (Banarescu et al., …...

We present the Stanford Question Answer- ing Dataset (SQuAD), a new reading compre- hension dataset consisting of 100,000+ ques- tions posed by crowdworkers on a set of Wikipedia articles, where …...

The problem of noisy and unbalanced train- ing data for supervised keyphrase extraction results from the subjectivity of keyphrase as- signment, which we quantify by crowdsourc- ing keyphrases for news …...

Entity linking is the task of identifying men- tions of entities in text, and linking them to entries in a knowledge base. This task is espe- cially difficult in microblogs, …...

We describe TweeTIME, a temporal tagger for recognizing and normalizing time expres- sions in Twitter. Most previous work in so- cial media analysis has to rely on tempo- ral resolvers …...

Coreference resolution is one of the first stages in deep language understanding and its impor- tance has been well recognized in the natu- ral language processing community. In this paper, …...

This paper describes the University of Sheffield’s submission to the SemEval 2016 Twitter Stance Detection weakly supervised task (SemEval 2016 Task 6, Subtask B). In stance detection, the goal is …...

Verb Phrase Ellipsis (VPE) is an anaphoric construction in which a verb phrase has been elided. It occurs frequently in dialogue and informal conversational settings, but despite its evident impact …...

AbstractWe introduce a new method for frame-semantic parsing that significantly improves the prior state of the art. Our model leverages the advantages of a deep bidirectional LSTM network which predicts …...

AbstractWe propose a sentiment analyzer for the prediction of document-level sentiments of English micro-blog messages from Twitter. The proposed method is based on lexicon integrated convolutional neural networks with attention …...

AbstractStandard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. To reward systems with real language …...

AbstractRecently emerged intelligent assistants on smartphones and home electronics (e.g., Siri and Alexa) can be seen as novel hybrids of domain-specific task-oriented spoken dialogue systems and open-domain non-task-oriented ones. To …...

AbstractIn the wake of a polarizing election, the cyber world is laden with hate speech. Context accompanying a hate speech text is useful for identifying hate speech, which however has …...

AbstractSemantic parsing shines at analyzing complex natural language that involves composition and computation over multiple pieces of evidence. However, datasets for semantic parsing contain many factoid questions that can be …...

AbstractIn our work we address limitations in the state-of-the-art in idiom type identification. We investigate different approaches for a lexical fixedness metric, a component of the state-of the-art model. We …...

AbstractThis paper describes our approach for SemEval-2017 Task 8. We aim at detecting the stance of tweets and determining the veracity of the given rumor. We utilize a convolutional neural …...

AbstractThe naive approach to annotation projection is not effective to project discourse annotations from one language to another because implicit relations are often changed to explicit ones and vice-versa in …...

AbstractBoth bottom-up and top-down strategies have been used for neural transition-based constituent parsing. The parsing strategies differ in terms of the order in which they recognize productions in the derivation …...

AbstractThis paper describes our participation in SemEval-2017 Task 10. We competed in Subtask 1 and 2 which consist respectively in identifying all the key phrases in scientific publications and label …...

AbstractFraming is a political strategy in which politicians carefully word their statements in order to control public perception of issues. Previous works exploring political framing typically analyze frame usage in …...

AbstractIn this paper, we present MayoNLP’s results from the participation in the ScienceIE share task at SemEval 2017. We focused on the keyphrase classification task (Subtask B). We explored semantic …...

This paper describes a system to extract aspect categories for the task of aspect based sentiment analysis. This system can extract both implicit and explicit aspects. We propose a one-vs-rest …...

AbstractThis paper describes our participation in SemEval-2017 Task 3 on Community Question Answering (cQA). The Question Similarity subtask (B) aims to rank a set of related questions retrieved by a …...

This paper describes the system details and results of team “EOF” from the Uni- versity of Melbourne for the shared task of ALTA 2017, which addresses the problem of text …...

AbstractOrganized relational knowledge in the form of “knowledge graphs” is important for many applications. However, the ability to populate knowledge bases with facts automatically extracted from documents has improved frustratingly …...

AbstractWhat prerequisite knowledge should students achieve a level of mastery before moving forward to learn subsequent coursewares? We study the extent to which the prerequisite relation between knowledge concepts in …...

AbstractRecognizing mentions of Adverse Drug Reactions (ADR) in social media is challenging: ADR mentions are context-dependent and include long, varied and unconventional descriptions as compared to more formal medical symptom …...

AbstractIn this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches. We demonstrate for common sequence tagging tasks that the seed value for the …...

AbstractClinical TempEval 2017 aimed to answer the question: how well do systems trained on annotated timelines for one medical condition (colon cancer) perform in predicting timelines on another medical condition …...

AbstractAutomated discourse analysis tools based on Natural Language Processing (NLP) aiming at the diagnosis of language-impairing dementias generally extract several textual metrics of narrative transcripts. However, the absence of sentence …...

AbstractIn this paper we present the TakeLab-QA entry to SemEval 2017 task 3, which is a question-comment re-ranking problem. We present a classification based approach, including two supervised learning models …...

AbstractThis study describes the design of the NTNU system for the ScienceIE task at the SemEval 2017 workshop. We use self-defined feature templates and multiple conditional random fields with extracted …...

AbstractWe introduce automatic verification as a post-processing step for entity linking (EL). The proposed method trusts EL system results collectively, by assuming entity mentions are mostly linked correctly, in order …...

AbstractWe describe an end-to-end pipeline processing approach for SemEval 2017’s Task 10 to extract keyphrases and their relations from scientific publications. We jointly identify and classify keyphrases by modeling the …...

AbstractAnimacy is a necessary property for a referent to be an agent, and thus animacy detection is useful for a variety of natural language processing tasks, including word sense disambiguation, …...

AbstractIn this paper, a new deep reinforcement learning based augmented general tagging system is proposed. The new system contains two parts: a deep neural network (DNN) based sequence labeling model …...

AbstractThis paper describes the system that we submitted for SemEval-2018 task 10: capturing discriminative attributes. Our system is built upon a simple idea of measuring the attribute word’s similarity with …...

AbstractThe “Capturing Discriminative Attributes” sharedtask is the tenth task, conjoint with SemEval2018. The task is to predict if a word can capture distinguishing attributes of one word from another. We …...

Implicit discourse relation recognition remains a challenging task as state-of-the-art approaches reach F1 scores ranging from 9.95% to 37.67% on the 2016 CoNLL shared task. In our work, we explore …...

Accurate prediction of suitable discourse connectives (however, furthermore, etc.) is a key component of any system aimed at building coherent and fluent discourses from shorter sentences and passages. As an …...

AbstractThis paper describes BomJi, a supervised system for capturing discriminative attributes in word pairs (e.g. yellow as discriminative for banana over watermelon). The system relies on an XGB classifier trained …...

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

AbstractIn this paper we describe our system for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1.1) and noisy data (subtask 1.2). We compare …...

Large amount of user-generated data are posted online in social media platforms, in- cluding user preferences, dining and leisure activities, events, news and personal blogs. This resulted in varying efforts …...

AbstractWe present a new dataset for machine comprehension in the medical domain. Our dataset uses clinical case reports with around 100,000 gap-filling queries about these cases. We apply several baselines …...

AbstractImplicit discourse relation recognition is a challenging task as the relation prediction without explicit connectives in discourse parsing needs understanding of text spans and cannot be easily derived from surface …...

AbstractThis paper describes a novel application of NLP models to detect denial of service attacks using only social media as evidence. Individual networks are often slow in reporting attacks, so …...

AbstractCapturing interactions among multiple predicate-argument structures (PASs) is a crucial issue in the task of analyzing PAS in Japanese. In this paper, we propose new Japanese PAS analysis models that …...

AbstractWe study German affixoids, a type of morpheme in between affixes and free stems. Several properties have been associated with them – increased productivity; a bleached semantics, which is often …...

AbstractThis paper describes our submissions for SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using NLP. The DM_NLP participated in two subtasks: SubTask 1 classifies if a sentence is useful …...

AbstractExtracting the information from text when an event happened is challenging. Documents do not only report on current events, but also on past events as well as on future events. …...

AbstractThis paper proposes a novel feature extraction process for SemEval task 3: Irony detection in English tweets. The proposed system incorporates a concatenative featurization of tweet and hashtags, which helps …...

AbstractSemi-supervised bootstrapping techniques for relationship extraction from text iteratively expand a set of initial seed instances. Due to the lack of labeled data, a key challenge in bootstrapping is semantic …...

AbstractCharacter identification is an entity-linking task that finds words referring to the same person among the nouns mentioned in a conversation and turns them into one entity. In this paper, …...

AbstractWe explore deception detection in interview dialogues. We analyze a set of linguistic features in both truthful and deceptive responses to interview questions. We also study the perception of deception, …...

AbstractLuminoso participated in the SemEval 2018 task on “Capturing Discriminative Attributes” with a system based on ConceptNet, an open knowledge graph focused on general knowledge. In this paper, we describe …...

AbstractRecognizing named entities in a document is a key task in many NLP applications. Although current state-of-the-art approaches to this task reach a high performance on clean text (e.g. newswire …...

Argumentative corpora are costly to create and are available in only few languages with English dominating the area. In this paper we release the first publicly available corpora in all …...

AbstractIn this paper, we describe Alibaba’s participating system in the semEval-2018 Task5: Counting Events and Participants in the Long Tail. We designed and implemented a pipeline system that consists of …...

AbstractIn conversational speech, the acoustic signal provides cues that help listeners disambiguate difficult parses. For automatically parsing spoken utterances, we introduce a model that integrates transcribed text and acoustic-prosodic features …...

AbstractWe propose a Long Short Term Memory Neural Network model for irony detection in tweets in this paper. Our model is trained using word embeddings and emoji embeddings. We show …...

AbstractWe describe our experiences in using an open domain question answering model (Chen et al., 2017) to evaluate an out-of-domain QA task of assisting in analyzing privacy policies of companies. …...

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

AbstractTo answer the question in machine comprehension (MC) task, the models need to establish the interaction between the question and the context. To tackle the problem that the single-pass model …...

Semantic Role Labeling (SRL) has been ex- tensively studied, mostly for understanding English formal language. However, only a few reports exist for informal conversational text, especially for language being used …...

AbstractThis paper presents the outcomes of the Parsing Time Normalization shared task held within SemEval-2018. The aim of the task is to parse time expressions into the compositional semantic graphs …...

AbstractThis article presents the SIRIUS-LTG-UiO system for the SemEval 2018 Task 7 on Semantic Relation Extraction and Classification in Scientific Papers. First we extract the shortest dependency path (sdp) between …...

AbstractQuestion answering systems deteriorate dramatically in the presence of adversarial sentences in articles. According to Jia and Liang (2017), the single BiDAF system (Seo et al., 2016) only achieves an …...

AbstractSearching for sentences containing claims in a large text corpus is a key component in developing an argumentative content search engine. Previous works focused on detecting claims in a small …...

AbstractState-of-the-art Natural Language Processing algorithms rely heavily on efficient word segmentation. Urdu is amongst languages for which word segmentation is a complex task as it exhibits space omission as well …...

AbstractWe present our UWB system for the task of capturing discriminative attributes at SemEval 2018. Given two words and an attribute, the system decides, whether this attribute is discriminative between …...

In a multilingual or sociolingual configura- tion Intra-sentential Code Switching (ICS) or Code Mixing (CM) is frequently observed nowadays. In the world most of the people know more than one …...

AbstractWe introduce a general framework for several information extraction tasks that share span representations using dynamically constructed span graphs. The graphs are dynamically constructed by selecting the most confident entity …...

AbstractWe tackle the nested and overlapping event detection task and propose a novel search-based neural network (SBNN) structured prediction model that treats the task as a search problem on a …...

AbstractSemantic role labeling (SRL) aims to identify the predicate-argument structure of a sentence. Inspired by the strong correlation between syntax and semantics, previous works pay much attention to improve SRL …...

AbstractWe propose an efficient neural framework for sentence-level discourse analysis in accordance with Rhetorical Structure Theory (RST). Our framework comprises a discourse segmenter to identify the elementary discourse units (EDU) …...

AbstractDe-identification is the task of detecting protected health information (PHI) in medical text. It is a critical step in sanitizing electronic health records (EHR) to be shared for research. Automatic …...

AbstractThis article describes Amobee’s participation in “HatEval: Multilingual detection of hate speech against immigrants and women in Twitter” (task 5) and “OffensEval: Identifying and Categorizing Offensive Language in Social Media” …...

AbstractTraditional approaches to automatic term extraction do not rely on machine learning (ML) and select the top n ranked candidate terms or candidate terms above a certain predefined cut-off point, …...

AbstractComplaining is a basic speech act regularly used in human and computer mediated communication to express a negative mismatch between reality and expectations in a particular situation. Automatically identifying complaints …...

This research aims to develop a parallel sentence extraction method for automatically extracting parallel sentence pairs from bilingual comparable corpora based on cross-lingual word embeddings. Our task is to effectively …...

AbstractDetecting emotion from dialogue is a challenge that has not yet been extensively surveyed. One could consider the emotion of each dialogue turn to be independent, but in this paper, …...

AbstractOffensive language has become pervasive in social media. In Offensive Language Identification tasks, it may be difficult to predict accurately only according to the surface words. So we try to …...

AbstractThe paper presents Columbia team’s participation in the SemEval 2019 Shared Task 7: RumourEval 2019. Detecting rumour on social networks has been a focus of research in recent years. Previous …...

AbstractThis paper proposes Confusionset-guided Pointer Networks for Chinese Spell Check (CSC) task. More concretely, our approach utilizes the off-the-shelf confusionset for guiding the character generation. To this end, our novel …...

AbstractHumans gather information through conversations involving a series of interconnected questions and answers. For machines to assist in information gathering, it is therefore essential to enable them to answer conversational …...

AbstractIn this paper, I describe a fusion model combining contextualized and static word representations for approaching the EmoContext task in the SemEval 2019 competition. The model is based on two …...

AbstractFor the spell correction task, vocabulary based methods have been replaced with methods that take morphological and grammar rules into account. However, such tools are fairly immature, and, worse, non-existent …...

AbstractRecognizing the internal structure of events is a challenging language processing task of great importance for text understanding. We present a supervised model for automatically identifying when one event is …...

AbstractIn a world of information operations, influence campaigns, and fake news, classification of news articles as following hyperpartisan argumentation or not is becoming increasingly important. We present a deep learning-based …...

AbstractTask 3, EmoContext, in the International Workshop SemEval 2019 provides training and testing datasets for the participant teams to detect emotion classes (Happy, Sad, Angry, or Others). This paper proposes …...

AbstractMessaging platforms like WhatsApp, Facebook Messenger and Twitter have gained recently much popularity owing to their ability in connecting users in real-time. The content of these textual messages can be …...

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

AbstractThis paper describes our system (Fermi) for Task 4: Hyper-partisan News detection of SemEval-2019. We use simple text classification algorithms by transforming the input features to a reduced feature set. …...

AbstractThis paper presents the models submitted by Ghmerti team for subtasks A and B of the OffensEval shared task at SemEval 2019. OffensEval addresses the problem of identifying and categorizing …...

AbstractGraph Convolutional Networks (GCNs) are a class of spectral clustering techniques that leverage localized convolution filters to perform supervised classification directly on graphical structures. While such methods model nodes’ local …...

AbstractIn this paper, we present GraphRel, an end-to-end relation extraction model which uses graph convolutional networks (GCNs) to jointly learn named entities and relations. In contrast to previous baselines, we …...

AbstractIn this paper we present an emotion classifier models that submitted to the SemEval-2019 Task 3 : EmoContext. Our approach is a Gated Recurrent Neural Network (GRU) model with attention …...

AbstractIdentifying what is at the center of the meaning of a word and what discriminates it from other words is a fundamental natural language inference task. This paper describes an …...

AbstractAs the online world continues its exponential growth, interpersonal communication has come to play an increasingly central role in opinion formation and change. In order to help users better engage …...

AbstractThis paper describes our system submissions as part of our participation (team name: JU_ETCE_17_21) in the SemEval 2019 shared task 6: “OffensEval: Identifying and Catego- rizing Offensive Language in Social …...

AbstractThis paper describes our approach to solve Semeval task 3: EmoContext; where, given a textual dialogue i.e. a user utterance along with two turns of context, we have to classify …...

AbstractThe task of humor recognition has attracted a lot of attention recently due to the urge to process large amounts of user-generated texts and rise of conversational agents. We collected …...

AbstractUser-generated reviews can be decomposed into fine-grained segments (e.g., sentences, clauses), each evaluating a different aspect of the principal entity (e.g., price, quality, appearance). Automatically detecting these aspects can be …...

AbstractIn recent years there is a surge of interest in applying distant supervision (DS) to automatically generate training data for relation extraction (RE). In this paper, we study the problem …...

AbstractAn event-noun is a noun that has an argument structure similar to a predicate. Recent works, including those considered state-of-the-art, ignore event-nouns or build a single model for solving both …...

AbstractWe show that constituency parsing benefits from unsupervised pre-training across a variety of languages and a range of pre-training conditions. We first compare the benefits of no pre-training, fastText, ELMo, …...

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

AbstractMachine comprehension of texts longer than a single sentence often requires coreference resolution. However, most current reading comprehension benchmarks do not contain complex coreferential phenomena and hence fail to evaluate …...

AbstractWhen searching for information, a human reader first glances over a document, spots relevant sections, and then focuses on a few sentences for resolving her intention. However, the high variance …...

AbstractArabizi is an informal written form of dialectal Arabic transcribed in Latin alphanumeric characters. It has a proven popularity on chat platforms and social media, yet it suffers from a …...

AbstractThe prevalence of informal language such as slang presents challenges for natural language systems, particularly in the automatic discovery of flexible word usages. Previous work has explored slang in terms …...

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 …...

AbstractIn this paper, we present a relationship extraction based methodology for table structure recognition in PDF documents. The proposed deep learning-based method takes a bottom-up approach to table recognition in …...

AbstractIn this paper we describe our participation in the SemEval 2019 shared task on hyperpartisan news detection. We present the system that we submitted for final evaluation and the three …...

AbstractText classification aims at mapping documents into a set of predefined categories. Supervised machine learning models have shown great success in this area but they require a large number of …...

AbstractThis paper describes a novel, syntax-based system for automatic detection and resolution of Noun Phrase Ellipsis (NPE) in English. The system takes in free input English text, detects the site …...

AbstractIn this paper, we present our submission for SemEval-2019 Task 4: Hyperpartisan News Detection. Hyperpartisan news articles are sharply polarized and extremely biased (onesided). It shows blind beliefs, opinions and …...

AbstractWe present the Visually Grounded Neural Syntax Learner (VG-NSL), an approach for learning syntactic representations and structures without any explicit supervision. The model learns by looking at natural images and …...

AbstractSuggestion mining task aims to extract tips, advice, and recommendations from unstructured text. The task includes many challenges, such as class imbalance, figurative expressions, context dependency, and long and complex …...

AbstractIn this paper we describe a deep-learning system that competed as SemEval 2019 Task 9-SubTask A: Suggestion Mining from Online Reviews and Forums. We use Word2Vec to learn the distributed …...

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

AbstractIn this paper, we present the result of our experiment with a variant of 1 Dimensional Convolutional Neural Network (Conv1D) hyper-parameters value. We describe the system entered by the team …...

AbstractThis paper introduces a corpus of Turkish offensive language. To our knowledge, this is the first corpus of offensive language for Turkish. The corpus consists of randomly sampled micro-blog posts …...

AbstractAuthorship attribution aims to identify the author of a text based on the stylometric analysis. Authorship obfuscation, on the other hand, aims to protect against authorship attribution by modifying a …...

AbstractOne of the primary tasks of morphological parsers is the disambiguation of homographs. Particularly difficult are cases of unbalanced ambiguity, where one of the possible analyses is far more frequent …...

Text classification is a popular topic of natural language processing, which has currently at- tracted numerous research efforts worldwide. The significant increase of data in social me- dia requires the …...

AbstractWe propose a novel linearization of a constituent tree, together with a new locally normalized model. For each split point in a sentence, our model computes the normalizer on all …...

AbstractWe present a novel supervised word alignment method based on cross-language span prediction. We first formalize a word alignment problem as a collection of independent predictions from a token in …...

AbstractHumans rarely perform better than chance at lie detection. To better understand human perception of deception, we created a game framework, LieCatcher, to collect ratings of perceived deception using a …...

AbstractIn specific domains, such as procedural scientific text, human labeled data for shallow semantic parsing is especially limited and expensive to create. Fortunately, such specific domains often use rather formulaic …...

AbstractThis paper explores an unsupervised approach to learning a compositional representation function for multi-word expressions (MWEs), and evaluates it on the Tratz dataset, which associates two-word expressions with the semantic …...

AbstractTraditional document similarity measures provide a coarse-grained distinction between similar and dissimilar documents. Typically, they do not consider in what aspects two documents are similar. This limits the granularity of …...

In this paper, we conducted an aspect-based sentiment analysis using Google Maps user reviews of Indonesia’s tourism destinations which are Borobudur and Prambanan Temple. The aspects we used are Attractions, …...

AbstractModern digital personal assistants interact with users through voice. Therefore, they heavily rely on automatic speech recognition (ASR) in order to convert speech to text and perform further tasks. We …...

AbstractWe present the first approach to automatically building resources for academic writing. The aim is to build a writing aid system that automatically edits a text so that it better …...

AbstractInnovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model in the …...

AbstractMemotion analysis is a very crucial and important subject in today’s world that is dominated by social media. This paper presents the results and analysis of the SemEval-2020 Task-8: Memotion …...

AbstractEmotions are an important element of human nature, often affecting the overall wellbeing of a person. Therefore, it is no surprise that the health domain is a valuable area of …...

AbstractWe present the first large scale corpus for entity resolution in email conversations (CEREC). The corpus consists of 6001 email threads from the Enron Email Corpus containing 36,448 email messages …...

AbstractBooks are typically segmented into chapters and sections, representing coherent sub-narratives and topics. We investigate the task of predicting chapter boundaries, as a proxy for the general task of segmenting …...

AbstractElementary Discourse Unit (EDU) recognition is the basic task of discourse analysis, and the Chinese and English discourse alignment corpus is helpful to the studies of EDU recognition. This paper …...

AbstractExploiting natural language processing in the clinical domain requires de-identification, i.e., anonymization of personal information in texts. However, current research considers de-identification and downstream tasks, such as concept extraction, only …...

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

Due to the lack of explicit end-of-sentence marker in Thai, Elementary Discourse Units (EDUs) are usually preferred over sentences as the basic linguistic units for processing Thai language. However, some …...

AbstractMany NLP tasks have benefited from transferring knowledge from contextualized word embeddings, however the picture of what type of knowledge is transferred is incomplete. This paper studies the types of …...

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

AbstractIn this paper, we present CorefQA, an accurate and extensible approach for the coreference resolution task. We formulate the problem as a span prediction task, like in question answering: A …...

AbstractIn this paper, we present the development of a training dataset for Dutch Named Entity Recognition (NER) in the archaeology domain. This dataset was created as there is a dire …...

AbstractUsing current methods, the construction of multilingual resources in FrameNet is an expensive and complex task. While crowdsourcing is a viable alternative, it is difficult to include non-native English speakers …...

AbstractThe growing popularity and applications of sentiment analysis of social media posts has naturally led to sentiment analysis of posts written in multiple languages, a practice known as code-switching. While …...

AbstractData augmentation is proven to be effective in many NLU tasks, especially for those suffering from data scarcity. In this paper, we present a powerful and easy to deploy text …...

AbstractIn this paper, we describe a methodology to predict sentiment in code-mixed tweets (hindi-english). Our team called verissimo.manoel in CodaLab developed an approach based on an ensemble of four models …...

AbstractThis paper describes participation in DeftEval 2020 (part of SemEval sharing task competition), and is focused on the sentence classification. Our approach to the task was to create an ensemble …...

AbstractWe release an urgency dataset that consists of English tweets relating to natural crises, along with annotations of their corresponding urgency status. Additionally, we release evaluation datasets for two low-resource …...

AbstractIn this paper, we study sequence-to-sequence (S2S) keyphrase generation models from the perspective of diversity. Recent advances in neural natural language generation have made possible remarkable progress on the task …...

AbstractThis paper describes our team work and submission for the SemEval 2020 (Sub-Task A) “Offensive Eval: Identifying and Categorizing Offensive Arabic Language in Arabic Social Media”. Our two baseline models …...

AbstractWith the transformation of education from the traditional classroom environment to online education and assessment, it is more and more important to accurately assess the difficulty of questions than ever. …...

AbstractRecently, many methods discover effective evidence from reliable sources by appropriate neural networks for explainable claim verification, which has been widely recognized. However, in these methods, the discovery process of …...

AbstractIn recent years, a new interesting task, called emotion-cause pair extraction (ECPE), has emerged in the area of text emotion analysis. It aims at extracting the potential pairs of emotions …...

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

AbstractThough people rarely speak in complete sentences, punctuation confers many benefits to the readers of transcribed speech. Unfortunately, most ASR systems do not produce punctuated output. To address this, we …...

AbstractWe propose a novel constituency parsing model that casts the parsing problem into a series of pointing tasks. Specifically, our model estimates the likelihood of a span being a legitimate …...

AbstractWe present an annotated corpus of English cooking recipe procedures, and describe and evaluate computational methods for learning these annotations. The corpus consists of 300 recipes written by members of …...

AbstractDomain knowledge is important for building Natural Language Processing (NLP) systems for low-resource settings, such as in the clinical domain. In this paper, a novel joint training method is introduced …...

AbstractEvent extraction (EE) is a crucial information extraction task that aims to extract event information in texts. Previous methods for EE typically model it as a classification task, which are …...

AbstractEvent extraction, which aims to identify event triggers of pre-defined event types and their arguments of specific roles, is a challenging task in NLP. Most traditional approaches formulate this task …...

AbstractSuppose we want to specify the inductive bias that married couples typically go on honeymoons for the task of extracting pairs of spouses from text. In this paper, we allow …...

AbstractIn this paper we focus on connective identification and sense classification for explicit discourse relations in German, as two individual sub-tasks of the overarching Shallow Discourse Parsing task. We successively …...

AbstractRecently, spoken language understanding (SLU) has attracted extensive research interests, and various SLU datasets have been proposed to promote the development. However, most of the existing methods focus on a …...

AbstractPrevious work on bridging anaphora recognition (Hou et al., 2013) casts the problem as a subtask of learning fine-grained information status (IS). However, these systems heavily depend on many hand-crafted …...

AbstractDocument structure extraction has been a widely researched area for decades with recent works performing it as a semantic segmentation task over document images using fully-convolution networks. Such methods are …...

AbstractNow that the performance of coreference resolvers on the simpler forms of anaphoric reference has greatly improved, more attention is devoted to more complex aspects of anaphora. One limitation of …...

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 …...

AbstractIn this paper, we present the results that the team IIITG-ADBU (codalab username ‘abaruah’) obtained in the SentiMix task (Task 9) of the International Workshop on Semantic Evaluation 2020 (SemEval …...

AbstractState-of-the-art multilingual models depend on vocabularies that cover all of the languages the model will expect to see at inference time, but the standard methods for generating those vocabularies are …...

AbstractConventional approaches to event detection usually require a fixed set of pre-defined event types. Such a requirement is often challenged in real-world applications, as new events continually occur. Due to …...

AbstractThe paper presents the solution of team ”Inno” to a SEMEVAL 2020 task 11 ”Detection of propaganda techniques in news articles”. The goal of the second subtask is to classify …...

AbstractThe paper describes systems that our team IRLab_DAIICT employed for the shared task Sentiment Analysis for Code-Mixed Social Media Text in SemEval 2020. We conducted our experiments on a Hindi-English …...

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

AbstractCode-mixing is a phenomenon which arises mainly in multilingual societies. Multilingual people, who are well versed in their native languages and also English speakers, tend to code-mix using English-based phonetic …...

AbstractThe obstetric Electronic Medical Record (EMR) contains a large amount of medical data and health information. It plays a vital role in improving the quality of the diagnosis assistant service. …...

AbstractIn this paper, we describe our approach to utilize pre-trained BERT models with Convolutional Neural Networks for sub-task A of the Multilingual Offensive Language Identification shared task (OffensEval 2020), which …...

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 …...

AbstractThis paper describes the participation of LIMSI_UPV team in SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text. The proposed approach competed in SentiMix HindiEnglish subtask, that addresses the …...

AbstractThis work revisits the task of training sequence tagging models with limited resources using transfer learning. We investigate several proposed approaches introduced in recent works and suggest a new loss …...

AbstractInternet memes have become a very popular mode of expression on social media networks today. Their multi-modal nature, caused by a mixture of text and image, makes them a very …...

AbstractThis paper describes our contribution to the SemEval-2020 Task 9 on Sentiment Analysis for Code-mixed Social Media Text. We investigated two approaches to solve the task of Hinglish sentiment analysis. …...

AbstractMetaphor as a cognitive mechanism in human’s conceptual system manifests itself an effective way for language communication. Although being intuitively sensible for human, metaphor detection is still a challenging task …...

AbstractModeling content importance is an essential yet challenging task for summarization. Previous work is mostly based on statistical methods that estimate word-level salience, which does not consider semantics and larger …...

AbstractResearch into the area of multiparty dialog has grown considerably over recent years. We present the Molweni dataset, a machine reading comprehension (MRC) dataset with discourse structure built over multiparty …...

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

AbstractIn contrast with the traditional single-grained word segmentation (SWS), where a sentence corresponds to a single word sequence, multi-grained Chinese word segmentation (MWS) aims to segment a sentence into multiple …...

AbstractDeep neural network-based pretraining methods have achieved impressive results in many natural language processing tasks including text classification. However, their applicability to large-scale text classification with numerous categories (e.g., several …...

AbstractWe describe a method for developing broad-coverage semantic dependency parsers for languages for which no semantically annotated resource is available. We leverage a multitask learning framework coupled with annotation projection. …...

AbstractThere is a large body of work on Biomedical Entity Recognition (Bio-NER) for English but there have only been a few attempts addressing NER for Chinese biomedical texts. Because of …...

AbstractNegation is an important characteristic of language, and a major component of information extraction from text. This subtask is of considerable importance to the biomedical domain. Over the years, multiple …...

AbstractWe propose neural models that can normalize text by considering the similarities of word strings and sounds. We experimentally compared a model that considers the similarities of both word strings …...

AbstractThis paper describes a neural network (NN) model that was used for participating in the OffensEval, Task 12 of the SemEval 2020 workshop. The aim of this task is to …...

AbstractThe Natural Questions (NQ) benchmark set brings new challenges to Machine Reading Comprehension: the answers are not only at different levels of granularity (long and short), but also of richer …...

AbstractThe presence of offensive language on social media platforms and the implications this poses is becoming a major concern in modern society. Given the enormous amount of content created every …...

AbstractMost supervised word sense disambiguation (WSD) systems build word-specific classifiers by leveraging labeled data. However, when using word-specific classifiers, the sparseness of annotations leads to inferior sense disambiguation performance on …...

AbstractNear-duplicate documents are particularly common in news media corpora. Editors often update wirefeed articles to address space constraints in print editions or to add local context; journalists often lightly modify …...

AbstractEzafe is a grammatical particle in some Iranian languages that links two words together. Regardless of the important information it conveys, it is almost always not indicated in Persian script, …...

AbstractThe discovery of supporting evidence for addressing complex mathematical problems is a semantically challenging task, which is still unexplored in the field of natural language processing for mathematical text. The …...

AbstractThe key to effortless end-user programming is natural language. We examine how to teach intelligent systems new functions, expressed in natural language. As a first step, we collected 3168 samples …...

AbstractThis paper explains our teams’ submission to the Shared Task of Fine-Grained Propaganda Detection in which we propose a sequential BERT-CRF based Span Identification model where the fine-grained detection is …...

AbstractThis paper presents Pyramid, a novel layered model for Nested Named Entity Recognition (nested NER). In our approach, token or text region embeddings are recursively inputted into L flat NER …...

AbstractThe task of concept prerequisite chain learning is to automatically determine the existence of prerequisite relationships among concept pairs. In this paper, we frame learning prerequisite relationships among concepts as …...

AbstractThis paper proposes a representation framework for encoding spatial language in radiology based on frame semantics. The framework is adopted from the existing SpatialNet representation in the general domain with …...

AbstractDocument-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence entities. However, effective aggregation of relevant information in the document …...

AbstractReproducibility is generally regarded as being a requirement for any form of experimental science. Even so, reproduction of research results is only recently beginning to be practiced and acknowledged. In …...

AbstractReading long documents to answer open-domain questions remains challenging in natural language understanding. In this paper, we introduce a new model, called RikiNet, which reads Wikipedia pages for natural question …...

AbstractWe present ScholarlyRead, span-of-word-based scholarly articles’ Reading Comprehension (RC) dataset with approximately 10K manually checked passage-question-answer instances. ScholarlyRead was constructed in semi-automatic way. We consider the articles from two popular …...

AbstractWe introduce a new classification task for scientific statements and release a large-scale dataset for supervised learning. Our resource is derived from a machine-readable representation of the arXiv.org collection of …...

AbstractInformation on social media comprises of various modalities such as textual, visual and audio. NLP and Computer Vision communities often leverage only one prominent modality in isolation to study social …...

AbstractIn this paper, we present the results of the SemEval-2020 Task 9 on Sentiment Analysis of Code-Mixed Tweets (SentiMix 2020). We also release and describe our Hinglish (Hindi-English)and Spanglish (Spanish-English) …...

AbstractThis paper describes a novel application of semi-supervision for shallow discourse parsing. We use a neural approach for sequence tagging and focus on the extraction of explicit discourse arguments. First, …...

AbstractWord alignments are useful for tasks like statistical and neural machine translation (NMT) and cross-lingual annotation projection. Statistical word aligners perform well, as do methods that extract alignments jointly with …...

AbstractWe present a simple few-shot named entity recognition (NER) system based on nearest neighbor learning and structured inference. Our system uses a supervised NER model trained on the source domain, …...

AbstractThis paper presents a solution for the Span Identification (SI) task in the “Detection of Propaganda Techniques in News Articles” competition at SemEval-2020. The goal of the SI task is …...

AbstractWarning: this paper contains content that may be offensive or upsetting. Language has the power to reinforce stereotypes and project social biases onto others. At the core of the challenge …...

AbstractBERT (Bidirectional Encoder Representations from Transformers) and related pre-trained Transformers have provided large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA). BERT is pretrained on two auxiliary …...

AbstractThe goal of open information extraction (OIE) is to extract facts from natural language text, and to represent them as structured triples of the form <subject,predicate, object>. For example, given …...

Diacritic restoration plays an important role in Natural Language Processing (NLP) for many diacritical languages such as Vietnamese, Czech, Hungarian, etc. With the develop- ment of deep neural network, this …...

AbstractAdvances in machine reading comprehension (MRC) rely heavily on the collection of large scale human-annotated examples in the form of (question, paragraph, answer) triples. In contrast, humans are typically able …...

AbstractQuestion Answering (QA) is in increasing demand as the amount of information available online and the desire for quick access to this content grows. A common approach to QA has …...

AbstractWe propose ThaiLMCut, a semi-supervised approach for Thai word segmentation which utilizes a bi-directional character language model (LM) as a way to leverage useful linguistic knowledge from unlabeled data. After …...

AbstractIn this paper, we propose an approach to automate the process of place name detection in the medical domain to enable epidemiologists to better study and model the spread of …...

AbstractCreating ontologies is an expensive task. Our vision is that we can automatically generate ontologies based on a set of relevant documents to create a kick-start in ontology creating sessions. …...

AbstractWe tackle the task of building supervised event trigger identification models which can generalize better across domains. Our work leverages the adversarial domain adaptation (ADA) framework to introduce domain-invariance. ADA …...

AbstractQuestion and answer generation is a data augmentation method that aims to improve question answering (QA) models given the limited amount of human labeled data. However, a considerable gap remains …...

AbstractWe propose a unified multilingual model for humor detection which can be trained under a transfer learning framework. 1) The model is built based on pre-trained multilingual BERT, thereby is …...

AbstractWe propose a method for unsupervised parsing based on the linguistic notion of a constituency test. One type of constituency test involves modifying the sentence via some transformation (e.g. replacing …...

AbstractWe show that state-of-the-art self-supervised language models can be readily used to extract relations from a corpus without the need to train a fine-tuned extractive head. We introduce RE-Flex, a …...

AbstractManipulative and misleading news have become a commodity for some online news outlets and these news have gained a significant impact on the global mindset of people. Propaganda is a …...

AbstractAspect-based sentiment analysis of review texts is of great value for understanding user feedback in a fine-grained manner. It has in general two sub-tasks: (i) extracting aspects from each review, …...

AbstractMixing languages are widely used in social media, especially in multilingual societies like India. Detecting the emotions contained in these languages, which is of great significance to the development of …...

AbstractIn this paper, we present an approach for sentiment analysis in code-mixed language on twitter defined in SemEval-2020 Task 9. Our team (referred as LiangZhao) employ different multilingual models with …...

AbstractIn many documents, such as semi-structured webpages, textual semantics are augmented with additional information conveyed using visual elements including layout, font size, and color. Prior work on information extraction from …...

AbstractThis paper reports the zyy1510 team’s work in the International Workshop on Semantic Evaluation (SemEval-2020) shared task on Sentiment analysis for Code-Mixed (Hindi-English, English-Spanish) Social Media Text. The purpose of …...

AbstractLinguistic typology is an area of linguistics concerned with analysis of and comparison between natural languages of the world based on their certain linguistic features. For that purpose, historically, the …...

AbstractThe deception in the text can be of different forms in different domains, including fake news, rumor tweets, and spam emails. Irrespective of the domain, the main intent of the …...

AbstractWe tackle the task of adapting event extractors to new domains without labeled data, by aligning the marginal distributions of source and target domains. As a testbed, we create two …...

AbstractIn the domain of natural language augmentation, the eligibility of generated samples remains not well understood. To gather insights around this eligibility issue, we apply a transformer-based similarity calculation within …...

AbstractWe present ARETA, an automatic error type annotation system for Modern Standard Arabic. We design ARETA to address Arabic’s morphological richness and orthographic ambiguity. We base our error taxonomy on …...

Due to a lack of annotated data, theories of historical syntax are often based on very small, manually compiled data sets. To enable the empirical evaluation of existing hypotheses, the …...

AbstractWe introduce AVA, an automatic evaluation approach for Question Answering, which given a set of questions associated with Gold Standard answers (references), can estimate system Accuracy. AVA uses Transformer-based language …...

Social spambots, an emerging class of spam- mers attempting to emulate people, are dif- ficult for both human annotators and classic bot detection techniques to reliably distinguish from genuine accounts. …...

AbstractScientific literature analysis needs fine-grained named entity recognition (NER) to provide a wide range of information for scientific discovery. For example, chemistry research needs to study dozens to hundreds of …...

AbstractWe consider the hierarchical representation of documents as graphs and use geometric deep learning to classify them into different categories. While graph neural networks can efficiently handle the variable structure …...

AbstractText-pair classification is the task of determining the class relationship between two sentences. It is embedded in several tasks such as paraphrase identification and duplicate question detection. Contemporary methods use …...

AbstractThis paper addresses the task of (complex) conversational question answering over a knowledge graph. For this task, we propose LASAGNE (muLti-task semAntic parSing with trAnsformer and Graph atteNtion nEworks). It …...

We introduce deInStance, a corpus of 1000 politicians’ answers in German (de) contain- ing sentences labeled with explicitly expressed and inferred stances - pro and con relations - by 3 …...

AbstractThis paper introduces the novel task of scene segmentation on narrative texts and provides an annotated corpus, a discussion of the linguistic and narrative properties of the task and baseline …...

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

AbstractDocument-level entity-based extraction (EE), aiming at extracting entity-centric information such as entity roles and entity relations, is key to automatic knowledge acquisition from text corpora for various domains. Most document-level …...

AbstractThis paper presents the DuluthNLP submission to Task 7 of the SemEval 2021 competition on Detecting and Rating Humor and Offense. In it, we explain the approach used to train …...

AbstractOnline conversations can sometimes take a turn for the worse, either due to systematic cultural differences, accidental misunderstandings, or mere malice. Automatically forecasting derailment in public online conversations provides an …...

Emotion stimulus extraction is a fine-grained subtask of emotion analysis that focuses on identifying the description of the cause behind an emotion expression from a text passage (e.g., in the …...

AbstractNon-neural approaches to argument mining (AM) are often pipelined and require heavy feature-engineering. In this paper, we propose a neural end-to-end approach to AM which is based on dependency parsing, …...

AbstractThis paper describes Humor-BERT, a set of BERT Large based models that we used in the SemEval-2021 Task 7: Detecting and Rating Humor and Offense. It presents pre and post …...

Identifying relations from dialogues is more challenging than traditional sentence-level re- lation extraction (RE), since the difficulties of speaker information representation and the long-range semantic reasoning. Despite the successful efforts, …...

AbstractDetection of toxic spans - detecting toxicity of contents in the granularity of tokens - is crucial for effective moderation of online discussions. The baseline approach for this problem using …...

AbstractPrevious works on key information extraction from visually rich documents (VRDs) mainly focus on labeling the text within each bounding box (i.e.,semantic entity), while the relations in-between are largely unexplored. …...

AbstractCommonsenseQA (CQA) (Talmor et al., 2019) dataset was recently released to advance the research on common-sense question answering (QA) task. Whereas the prior work has mostly focused on proposing QA …...

Existing datasets and methods that aim at the identification of time expressions in natural language text do not pay particular attention to expressions that are imprecise and that can- not …...

AbstractIn several question answering benchmarks, pretrained models have reached human parity through fine-tuning on an order of 100,000 annotated questions and answers. We explore the more realistic few-shot setting, where …...

AbstractRecent efforts have shown that neural text processing models are vulnerable to adversarial examples, but the nature of these examples is poorly understood. In this work, we show that adversarial …...

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

AbstractThis paper introduces the result of Team Grenzlinie’s experiment in SemEval-2021 task 7: HaHackathon: Detecting and Rating Humor and Offense. This task has two subtasks. Subtask1 includes the humor detection …...

AbstractThis paper presents our system submission to task 5: Toxic Spans Detection of the SemEval-2021 competition. The competition aims at detecting the spans that make a toxic span toxic. In …...

AbstractDetecting online hate is a difficult task that even state-of-the-art models struggle with. Typically, hate speech detection models are evaluated by measuring their performance on held-out test data using metrics …...

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

AbstractMulti-hop reasoning requires aggregation and inference from multiple facts. To retrieve such facts, we propose a simple approach that retrieves and reranks set of evidence facts jointly. Our approach first …...

AbstractRecent progress in deep learning has primarily been fueled by the availability of large amounts of annotated data that is obtained from highly expensive manual annotating pro-cesses. To tackle this …...

AbstractDistantly supervised models are very popular for relation extraction since we can obtain a large amount of training data using the distant supervision method without human annotation. In distant supervision, …...

AbstractEntity Linking (EL) refers to the task of linking entity mentions in the text to the correct entities inthe Knowledge Base (KB) in which entity embeddings play a vital and …...

AbstractNeural image-to-text radiology report generation systems offer the potential to improve radiology reporting by reducing the repetitive process of report drafting and identifying possible medical errors. However, existing report generation …...

AbstractDespite recent progress, state-of-the-art question answering models remain vulnerable to a variety of adversarial attacks. While dynamic adversarial data collection, in which a human annotator tries to write examples that …...

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

AbstractOne of the main challenges in conversational question answering (CQA) is to resolve the conversational dependency, such as anaphora and ellipsis. However, existing approaches do not explicitly train QA models …...

AbstractWet laboratory protocols (WLPs) are critical for conveying reproducible procedures in biological research. They are composed of instructions written in natural language describing the step-wise processing of materials by specific …...

Real-world machine learning systems are achieving remarkable performance in terms of coarse-grained metrics like overall accuracy and F-1 score. However, model improvement and development often require fine-grained modeling on individual …...

AbstractRecurrent Neural Networks (RNN) have been widely used in various Natural Language Processing (NLP) tasks such as text classification, sequence tagging, and machine translation. Long Short Term Memory (LSTM), a …...

AbstractOpen Information Extraction (OpenIE) aims to discover textual facts from a given sentence. In essence, the facts contained in plain text are unordered. However, the popular OpenIE systems usually output …...

AbstractDialog act prediction in open-domain conversations is an essential language comprehension task for both dialog system building and discourse analysis. Previous dialog act schemes, such as SWBD-DAMSL, are designed mainly …...

AbstractQuestion Answering (QA) tasks requiring information from multiple documents often rely on a retrieval model to identify relevant information for reasoning. The retrieval model is typically trained to maximize the …...

AbstractA recent topic of research in natural language generation has been the development of automatic response generation modules that can automatically respond to a user’s utterance in an empathetic manner. …...

AbstractIn this paper, we study the task of graph-based constituent parsing in the setting that binarization is not conducted as a pre-processing step, where a constituent tree may consist of …...

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

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

We propose two fast neural combinatory mod- els for constituency parsing: binary and multi- branching. Our models decompose the bottom- up parsing process into 1) classification of tags, labels, and …...

AbstractWe leverage a BLSTM with attention to identify toxic spans in texts. We explore different dimensions which affect the model’s performance. The first dimension explored is the toxic set the …...

AbstractToxicity detection of text has been a popular NLP task in the recent years. In SemEval-2021 Task-5 Toxic Spans Detection, the focus is on detecting toxic spans within English passages. …...

AbstractWord sense disambiguation (WSD) is a long-standing problem in natural language processing. One significant challenge in supervised all-words WSD is to classify among senses for a majority of words that …...

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

AbstractWe present a new human-human dialogue dataset - PhotoChat, the first dataset that casts light on the photo sharing behavior in online messaging. PhotoChat contains 12k dialogues, each of which …...

AbstractWe study Comparative Preference Classification (CPC) which aims at predicting whether a preference comparison exists between two entities in a given sentence and, if so, which entity is preferred over …...

AbstractThis paper proposes a new task regarding event reason extraction from document-level texts. Unlike the previous causality detection task, we do not assign target events in the text, but only …...

AbstractParsing spoken dialogue poses unique difficulties, including disfluencies and unmarked boundaries between sentence-like units. Previous work has shown that prosody can help with parsing disfluent speech (Tran et al. 2018), …...

AbstractMost of the recent work on personality detection from online posts adopts multifarious deep neural networks to represent the posts and builds predictive models in a data-driven manner, without the …...

AbstractThis paper describes PTST, a source-free unsupervised domain adaptation technique for sequence tagging, and its application to the SemEval-2021 Task 10 on time expression recognition. PTST is an extension of …...

AbstractSound event detection (SED) system outputs sound events and their time boundaries in audio signals. We proposed an RCRNN-based SED system with residual connection and convolution block attention mechanism based …...

AbstractWe point out that common evaluation practices for cross-document coreference resolution have been unrealistically permissive in their assumed settings, yielding inflated results. We propose addressing this issue via two evaluation …...

AbstractAn understanding of humor is an essential component of human-facing NLP systems. In this paper, we investigate several methods for detecting humor in short statements as part of Semeval-2021 Shared …...

AbstractThe SemEval 2021 task 5: Toxic Spans Detection is a task of identifying considered-toxic spans in text, which provides a valuable, automatic tool for moderating online contents. This paper represents …...

AbstractThis paper presents one of the top winning solution systems for task 7 at SemEval2021, HaHackathon: Detecting and Rating Humor and Offense. This competition is divided into two tasks, task1 …...

AbstractQuestion answering from semi-structured tables can be seen as a semantic parsing task and is significant and practical for pushing the boundary of natural language understanding. Existing research mainly focuses …...

AbstractThis paper describes our contribution to SemEval-2021 Task 5: Toxic Spans Detection. Our approach considers toxic spans detection as a segmentation problem. The system, Waw-unet, consists of a 1-D convolutional …...

AbstractSpelling Error Correction (SEC) that requires high-level language understanding is a challenging but useful task. Current SEC approaches normally leverage a pre-training then fine-tuning procedure that treats data equally. By …...

AbstractContextualised word embeddings is a powerful tool to detect contextual synonyms. However, most of the current state-of-the-art (SOTA) deep learning concept extraction methods remain supervised and underexploit the potential of …...

AbstractA sufficient amount of annotated data is usually required to fine-tune pre-trained language models for downstream tasks. Unfortunately, attaining labeled data can be costly, especially for multiple language varieties and …...

AbstractThere is currently a gap between the natural language expression of scholarly publications and their structured semantic content modeling to enable intelligent content search. With the volume of research growing …...

AbstractIn this work, we present a Serbian literary corpus that is being developed under the umbrella of the “Distant Reading for European Literary History” COST Action CA16204. Using this corpus …...

AbstractPrevious pre-neural work on structured prediction has produced very effective supervised clustering algorithms using linear classifiers, e.g., structured SVM or perceptron. However, these cannot exploit the representation learning ability of …...

AbstractOpen pit mines left many regions worldwide inhospitable or uninhabitable. Many sites are left behind in a hazardous or contaminated state, show remnants of waste, or have other restrictions imposed …...

AbstractHybrid data combining both tabular and textual content (e.g., financial reports) are quite pervasive in the real world. However, Question Answering (QA) over such hybrid data is largely neglected in …...

There is a recent interest in investigating few- shot NER, where the low-resource target do- main has different label sets compared with a resource-rich source domain. Existing meth- ods use …...

AbstractTracking entities throughout a procedure described in a text is challenging due to the dynamic nature of the world described in the process. Firstly, we propose to formulate this task …...

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 …...

AbstractIn low-resource settings, model transfer can help to overcome a lack of labeled data for many tasks and domains. However, predicting useful transfer sources is a challenging problem, as even …...

AbstractContemporary tobacco-related studies are mostly concerned with a single social media platform while missing out on a broader audience. Moreover, they are heavily reliant on labeled datasets, which are expensive …...

AbstractWe present our works on SemEval-2021 Task 5 about Toxic Spans Detection. This task aims to build a model for identifying toxic words in whole posts. We use the BiLSTM-CRF …...

AbstractEvent factuality indicates the degree of certainty about whether an event occurs in the real world. Existing studies mainly focus on identifying event factuality at sentence level, which easily leads …...

AbstractMeasuring document similarity plays an important role in natural language processing tasks. Most existing document similarity approaches suffer from the information gap caused by context and vocabulary mismatches when comparing …...

AbstractHumour detection is an interesting but difficult task in NLP. Because humorous might not be obvious in text, it can be embedded into context, hide behind the literal meaning and …...

AbstractDetecting humor is a challenging task since words might share multiple valences and, depending on the context, the same words can be even used in offensive expressions. Neural network architectures …...

AbstractIn recent years, the widespread use of social media has led to an increase in the generation of toxic and offensive content on online platforms. In response, social media platforms …...

AbstractWhile relation extraction is an essential task in knowledge acquisition and representation, and new-generated relations are common in the real world, less effort is made to predict unseen relations that …...

AbstractFrame semantic parsing is a fundamental NLP task, which consists of three subtasks: frame identification, argument identification and role classification. Most previous studies tend to neglect relations between different subtasks …...

AbstractThis paper describes the first publicly available corpus of Hmong, a minority language of China, Vietnam, Laos, Thailand, and various countries in Europe and the Americas. The corpus has been …...

AbstractThe phenomenon of compounding is ubiquitous in Sanskrit. It serves for achieving brevity in expressing thoughts, while simultaneously enriching the lexical and structural formation of the language. In this work, …...

AbstractLabeled data for the task of Coreference Resolution is a scarce resource, requiring significant human effort. While state-of-the-art coreference models rely on such data, we propose an approach that leverages …...

AbstractThis paper introduces a new Turkish Twitter Named Entity Recognition dataset. The dataset, which consists of 5000 tweets from a year-long period, was labeled by multiple annotators with a high …...

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

AbstractWord embeddings learned using the distributional hypothesis (e.g., GloVe, Word2vec) do not capture the affective dimensions of valence, arousal, and dominance, which are present inherently in words. We present a …...

AbstractLanguage identification (LID) is a crucial precursor for NLP, especially for mining web data. Problematically, most of the world’s 7000+ languages today are not covered by LID technologies. We address …...

AbstractDue to the widespread usage of social media sites and the enormous number of users who utilize irony implicit words in most of their tweets and posts, it has become …...

AbstractPre-trained language models have recently shown that training on large corpora using the language modeling objective enables few-shot and zero-shot capabilities on a variety of NLP tasks, including commonsense reasoning …...

One of the most important factors that sig- nificantly affects the quality of sequence la- beling models is the selection and encod- ing of input features. The complexity and morphological …...

Millions of reviews are generated on e- commerce platforms and analyzing them deeply brings a lot of useful information for sellers and buyers. This paper deals with the aspect-based sentiment …...

AbstractSocial media platforms and online streaming services have spawned a new breed of Hate Speech (HS). Due to the massive amount of user-generated content on these sites, modern machine learning …...

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

AbstractThe paper aims to identify cited text spans in the reference paper related to the given citance in the citing paper. We refer to it as cited text span retrieval …...

AbstractTo cope with the COVID-19 pandemic, many jurisdictions have introduced new or altered existing legislation. Even though these new rules are often communicated to the public in news articles, it …...

AbstractThis paper presents a solution for the SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition. What is challenging in this task is detecting semantically ambiguous and complex entities in short …...

AbstractFact-checking has gained increasing attention due to the widespread of falsified information. Most fact-checking approaches focus on claims made in English only due to the data scarcity issue in other …...

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

AbstractThis paper introduces CoRoSeOf, a large corpus of Romanian social media manually annotated for sexist and offensive language. We describe the annotation process of the corpus, provide initial analyses, and …...

AbstractRelation extraction is a core problem for natural language processing in the biomedical domain. Recent research on relation extraction showed that prompt-based learning improves the performance on both fine-tuning on …...

AbstractThe IMPRESSIONS section of a radiology report about an imaging study is a summary of the radiologist’s reasoning and conclusions, and it also aids the referring physician in confirming or …...

AbstractEvent extraction aims to identify an event and then extract the arguments participating in the event. Despite the great success in sentence-level event extraction, events are more naturally presented in …...

AbstractWe propose, DocQueryNet, a value retrieval method with arbitrary queries for form-like documents to reduce human effort of processing forms. Unlike previous methods that only address a fixed set of …...

AbstractDocument-level Relation Extraction (DocRE) is a more challenging task compared to its sentence-level counterpart. It aims to extract relations from multiple sentences at once. In this paper, we propose a …...

AbstractThis paper describes the participation of team DUCS at SemEval 2022 Task 6: iSarcasmEval - Intended Sarcasm Detection in English and Arabic. Team DUCS participated in SubTask A of iSarcasmEval …...

In Word Sense Disambiguation (WSD), several studies have proposed systems that incorporate dictionary glosses. The use of glosses expressed in short sentences can improve the accuracy of the WSD system. …...

AbstractTo maximize the accuracy and increase the overall acceptance of text classifiers, we propose a framework for the efficient, in-operation moderation of classifiers’ output. Our framework focuses on use cases …...

AbstractWe present EpidBioBERT, a biosurveillance epidemiological document tagger for disease surveillance over PADI-Web system. Our model is trained on PADI-Web corpus which contains news articles on Animal Diseases Outbreak extracted …...

AbstractMulti-hop Question Answering is an agent task for testing the reasoning ability. With the development of pre-trained models, the implicit reasoning ability has been surprisingly improved and can even surpass …...

AbstractThe proliferation of fake news, i.e., news intentionally spread for misinformation, poses a threat to individuals and society. Despite various fact-checking websites such as PolitiFact, robust detection techniques are required …...

AbstractLocal models for Entity Disambiguation (ED) have today become extremely powerful, in most part thanks to the advent of large pre-trained language models. However, despite their significant performance achievements, most …...

AbstractSpace situational awareness typically makes use of physical measurements from radar, telescopes, and other assets to monitor satellites and other spacecraft for operational, navigational, and defense purposes. In this work …...

AbstractAnswering questions over financial reports containing both tabular and textual data (hybrid data) is challenging as it requires models to select information from financial reports and perform complex quantitative analyses. …...

AbstractWe present a generalized paradigm for adaptation of propositional analysis (predicate-argument pairs) to new tasks and domains. We leverage an analogy between stances (belief-driven sentiment) and concerns (topical issues with …...

AbstractHate speech detection has gained increasing attention with the growing prevalence of hateful contents. When a text contains an obvious hate word or expression, it is fairly easy to detect …...

AbstractSarcasm refers to the use of words that have different literal and intended meanings. It represents the usage of words that are opposite of what is literally said, especially in …...

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

AbstractThe dialogue-based relation extraction (DialogRE) task aims to predict the relations between argument pairs that appear in dialogue. Most previous studies utilize fine-tuning pre-trained language models (PLMs) only with extensive …...

AbstractIt is well-known that the deep learning-based optical character recognition (OCR) system needs a large amount of data to train a high-performance character recognizer. However, it is costly to collect …...

AbstractThe pre-trained language model has recently dominated most downstream tasks in the NLP area. Particularly, bidirectional Encoder Representations from Transformers (BERT) is the most iconic pre-trained language model among the …...

AbstractMany NLP main tasks benefit from an accurate understanding of temporal expressions, e.g., text summarization, question answering, and information retrieval. This paper introduces Hengam, an adversarially trained transformer for Persian …...

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

AbstractTimeML is a scheme for representing temporal information (times, events, & temporal relations) in texts. Although automatic TimeML annotation is challenging, there has been notable progress, with F1s of 0.8–0.9 …...

AbstractThe recent success of large language models for text generation poses a severe threat to academic integrity, as plagiarists can generate realistic paraphrases indistinguishable from original work. However, the role …...

AbstractIn this paper we present our approach and system description on Task 5 A in MAMI: Multimedia Automatic Misogyny Identification. In our experiments we compared several architectures based on deep …...

AbstractIn this paper we present our approach and system description on iSarcasmEval: a SemEval task for intended sarcasm detection on social networks. This derives from our participation in SubTask A: …...

AbstractThis paper studies the (often implicit) human values behind natural language arguments, such as to have freedom of thought or to be broadminded. Values are commonly accepted answers to why …...

AbstractTo perform well on a machine reading comprehension (MRC) task, machine readers usually require commonsense knowledge that is not explicitly mentioned in the given documents. This paper aims to extract …...

AbstractThe goal of text zoning is to segment a text into zones (i.e., Background, Conclusion) that serve distinct functions. Argumentative zoning, a specific text zoning scheme for the scientific domain, …...

AbstractPrompting methods recently achieve impressive success in few-shot learning. These methods modify input samples with prompt sentence pieces, and decode label tokens to map samples to corresponding labels. However, such …...