Разрешение многозначности слов

We describe a method for obtaining subject-dependent word sets relative to some (subjecO domain. Using the subject classifications given in the machine-readable ver- sion of Longman's Dictionary of Contemporary English, …...

A comparison W~LS made of vectors derived by using ordinary co-occurrence statistics from large text cor- pora and of vectors derived by measuring the inter- word distances in dictionary definitions. …...

Most probabilistic classifiers used for word-sense disam- biguation have either been based on only one contextual feature or have used a model that is simply assumed to characterize the interdependencies …...

In this paper, we describe an algorithm for aligning words with their translation in a bilingual corpus. Existing algorithms require enormous bilingual data to train statistical word-to-word translation models. Using …...

In this paper, we propose a statistical approach for clustering of artMes us- ing on-line dictionary definitions. One of the characteristics of our approach is that every sense of word …...

This paper describes a new approach to word sense disambiguation (WSD) based on automatically acquired "word sense division. The semantically related sense entries in a bilingual dictionary are arranged in …...

In this paper, we present a new approach for word sense disambiguation (WSD) us- ing an exemplar-based learning algorithm. This approach integrates a diverse set of knowledge sources to disambiguate …...

This paper describes measures for evaluating the three determinants of how well a probabilistic elas- sifter performs on a given test set. These determi- nants are the appropriateness, for the …...

In this paper, we report recent improve- ments to the exemplar-based learning ap- proach for word sense disambiguation that have achieved higher disambiguation accu- racy. By using a larger value …...

Statistical models of word-sense disam- biguation are often based on a small num- ber of contextual features or on a model that is assumed to characterize the inter- actions among …...

In this paper we describe a method for per- forming word sense disambiguation (WSD). The method relies on unsupervised learning and ex- ploits functional relations among words as pro- duced …...

This paper proposes a method to over- come the drawbacks of WordNet when applied to information retrieval by com- plementing it with Roget's thesaurus and corpus-derived thesauri. Words and rela- …...

In this paper, we propose a practical method to detect Japanese homophone errors in Japanese texts. It is very important to detect homophone errors in Japanese revision systems because Japanese …...

This paper describes a first attempt at a sta- tistical model for simultaneous syntactic pars- ing and generalized word-sense disambigna- tion. On a new data set we have constructed for …...

This paper revisits the one sense per collocation hypothesis using fine-grained sense distinctions and two different corpora. We show that the hypothesis is weaker for fine-grained sense distinctions (70% vs. …...

Usually, there are various non-alphabet symbols (“/”, “:”, “-”, etc.) occurring in Mandarin texts. Such symbols may be pronounced more than one oral expression with respect to its sense category. …...

Aligned parallel corpora have proved very useful in many natural language processing tasks, including statistical machine translation and word sense disambiguation. In this paper, we describe an alignment technique for …...

This paper describes a system for word sense disambiguation that participated in the Swedish Lexical Sample task of SENSEVAL-2. The system LIU-WSD is based on letting different contextual features cast …...

The approach presented in this paper for Word Sense Disambiguation (WSD) is based on a combination of different views of the context. Semantic Classification Trees (SCT) are employed over a …...

Selectional preference learning methods have usually focused on word- to-class relations, e.g., a verb selects as its subject a given nominal class. This papers extends previous statistical models to class-to-class …...

This paper describes the all-word sense disam- biguation task provided by Estonian team at SENSEVAL-2. About 10,000 words are manually disambiguated according to Estonian WordN et word senses. Language-specific problems …...

CL Research's word-sense disambiguation (WSD) system is part of the DIMAP dictionary software, designed to use any full dictionary as the basis for unsupervised disambiguation. Official SENSEV AL-2 results were …...

The major goal in ITC-irst's participation at SENSEVAL-2 was to test the role of domain in- formation in word sense disambiguation. The underlying working hypothesis is that domain labels, such …...

We present WASP-Bench: a novel approach to Word Sense Disambiguation, also providing a semi-automatic environment for a lexicographer to compose dictionary entries based on corpus evidence. For WSD, involving lexicographers …...

In this paper, we evaluate a vari- ety of knowledge sources and super- vised learning algorithms for word sense disambiguation on SENSEVAL-2 and SENSEVAL-1 data. Our knowledge sources include the …...

This paper demonstrates the substantial empirical success of classifier combination for the word sense disambiguation task. It investigates more than 10 classifier combination methods, including second order classifier stacking, over …...

This paper explores the contribution of a broad range of syntactic features to WSD: grammatical relations coded as the presence of adjuncts/arguments in isolation or as subcategorization frames, and instantiated …...

An unsupervised method for word sense disam- biguation using a bilingual comparable corpus was developed. First, it extracts statistically significant pairs of related words from the corpus of each lan- …...

Word sense is ambiguous in natural language processing (NLP). This phenomenon is particularly keen in cases involving noun-verb (NV) word-pairs. This paper describes a sense-based noun-verb event frame (NVEF) identifier …...

It is popular in WSD to use contextual information in training sense tagged data. Co-occurring words within a limited window-sized context support one sense among the semantically ambiguous ones of …...

A central problem of word sense disam- biguation (WSD) is the lack of manually sense-tagged data required for supervised learning. In this paper, we evaluate an ap- proach to automatically …...

We present a supervised machine learning algorithm for metonymy resolution, which exploits the similarity between examples of conventional metonymy. We show that syntactic head-modifier relations are a high precision feature …...

In this paper, we present an application- oriented evaluation of three Part-of- Speech (PoS) taggers in a word sense disambiguation (WSD) system. Follow- ing the intuition that high quality input …...

Today, corpus plays an important role in development and evaluation language and speech technologies, such as part of speech tagging, parsing, word sense disambiguation, text categorization, named entity classification, information …...

The task of word sense disambiguation is to assign a sense label to a word in a passage. We report our algorithms and experiments for the two tasks that we …...

In this paper, we present a corpus-based super- vised word sense disambiguation (WSD) sys- tem for Dutch which combines statistical classi- fication (maximum entropy) with linguistic in- formation. Instead of …...

In this paper we show that an unsupervised method for ranking word senses automatically can be used to iden- tify infrequently occurring senses. We demonstrate this using a ranking of …...

This paper investigates the application of co- training and self-training to word sense disam- biguation. Optimal and empirical parameter se- lection methods for co-training and self-training are investigated, with various …...

The success of supervised learning approaches to word sense disambiguation is largely de- pendent on the features used to represent the context in which an ambiguous word occurs. Previous work …...

Traditionally, word sense disambiguation (WSD) involves a different context model for each individual word. This paper presents a new approach to WSD using weakly supervised learning. Statistical models are not …...

In word sense disambiguation (WSD), the heuristic of choosing the most common sense is extremely powerful because the distribution of the senses of a word is often skewed. The problem …...

The paper presents a method for word sense disambiguation based on parallel corpora. The method exploits recent advances in word alignment and word clustering based on automatic extraction of translation …...

Word Sense Disambiguation (WSD) is the task of choosing the right sense of an ambiguous word given a context. Using Naive Bayesian (NB) classifiers is known as one of the …...

This paper proposes unsupervised word sense disambiguation based on automati- cally constructed case frames and its in- corporation into our zero pronoun resolu- tion system. The word sense disambigua- tion …...

This paper proposes a method to improve the robustness of a word sense disambigua- tion (WSD) system for Japanese. Two WSD classifiers are trained from a word sense-tagged corpus: one …...

This paper presents a new open text word sense disambiguation method that combines the use of logical inferences with PageRank-style algo- rithms applied on graphs extracted from natu- ral language …...

In this paper, we introduce a new semi-supervised learning model for word sense disambiguation based on Kernel Prin- cipal Component Analysis (KPCA), with experiments showing that it can further improve …...

This article studies different aspects of a new approach for word sense disambiguation using statistical information gains from a target language monolingual corpus. Here, the source language is English and …...

This paper presents the Swarthmore College word- sense disambiguation system which was designed for the 2004 SENSEVAL3 competition. Our system participated in five tasks: the lexical sample tasks in Basque, …...

The DLSI-UA team is currently working on sev- eral word sense disambiguation approaches, both supervised and unsupervised. These approaches are based on different ways to use both annotated and unannotated …...

Classifier combination is a promising way to improve performance of word sense disambiguation. We propose a new combinational method in this paper. We first construct a series of Naïve Bayesian …...

The Naïve Bayes classification proves to be a good performing tool in word sense disambiguation, although it has not yet been applied to the Romanian language. The aim of this …...

We describe two probabilistic models for unsuper- vised word-sense disambiguation using parallel cor- pora. The first model, which we call the Sense model, builds on the work of Diab and …...

This paper explores the large-scale acquisition of sense-tagged examples for Word Sense Disam- biguation (WSD). We have applied the “WordNet monosemous relatives” method to construct auto- matically a web corpus …...

In word sense disambiguation (WSD), the heuristic of choosing the most common sense is extremely powerful because the distribution of the senses of a word is often skewed. The first …...

In this paper we present a supervised Word Sense Disambiguation methodol- ogy, that exploits kernel methods to model sense distinctions. In particular a combi- nation of kernel functions is adopted …...

Word Sense Disambiguation suffers from a long-standing problem of knowledge ac- quisition bottleneck. Although state of the art supervised systems report good accu- racies for selected words, they have not …...

We describe an automatic Word Sense Disambiguation (WSD) system that dis- ambiguates verb senses using syntactic and semantic features that encode infor- mation about predicate arguments and se- mantic classes. …...

Traditionally, word sense disambiguation (WSD) involves a different context classifi- cation model for each individual word. This paper presents a weakly supervised learning approach to WSD based on learning a …...

. This paper describes a novel method for a word sense disam- biguation that utilizes relatives (i.e. synonyms, hypernyms, meronyms, etc in WordNet) of a target word and raw corpora. …...

This paper presents a new approach based on Equivalent Pseudowords (EPs) to tackle Word Sense Disambiguation (WSD) in Chinese language. EPs are par- ticular artificial ambiguous words, which can be …...

This paper presents a new application of the recently proposed machine learning method Alternating Structure Optimiza- tion (ASO), to word sense disambiguation (WSD). Given a set of WSD problems and …...

. Multiple target translations are due to several meanings of source words, and various target word equivalents depending on the context of the source word. Thus, an automated approach is …...

AbstractKB-N is a web-accessible searchable Knowledge Bank comprising A) a parallel corpus of quality assured and calibrated English and Norwegian text drawn from economic-administrative knowledge domains, and B) a domain-focused …...

In this paper we approach word sense disambiguation and information extrac- tion as a unified tagging problem. The task consists of annotating text with the tagset defined by the 41 …...

. In an EBMT system, we will meet the word sense disambiguation problem. The disambiguation methods used at present can’t be used easily in EBMT. We propose a new method …...

Fine-grained sense distinctions are one of the major obstacles to successful Word Sense Disambiguation. In this paper, we present a method for reducing the granularity of the WordNet sense inven- …...

While it is generally agreed that Word Sense Disambiguation (WSD) is an application-dependent task, the great majority of systems pursue application-independent approaches. We propose a strategy to support WSD for …...

The current situation for Word Sense Dis- ambiguation (WSD) is somewhat stuck due to lack of training data. We present in this paper a novel disambiguation al- gorithm that improves …...

Subjectivity and meaning are both impor- tant properties of language. This paper ex- plores their interaction, and brings empir- ical evidence in support of the hypotheses that (1) subjectivity is …...

Graph-based semi-supervised learning has recently emerged as a promising approach to data-sparse learning problems in natu- ral language processing. All graph-based algorithms rely on a graph that jointly rep- resents …...

When a word sense disambiguation (WSD) system is trained on one domain but ap- plied to a different domain, a drop in ac- curacy is frequently observed. This high- lights …...

Data sparsity is one of the main factors that make word sense disambiguation (WSD) difficult. To overcome this problem we need to find effective ways to use resources other than …...

This document describes the Word Sense Disam- biguation system used by Language Computer Cor- poration at English Coarse Grained All Word Task at SemEval 2007. The system is based on …...

We present a novel approach to the word sense disambiguation problem which makes use of corpus-based evidence com- bined with background knowledge. Em- ploying an inductive logic programming algorithm, the …...

This paper describes the clustering-based approach to Word Sense Disambiguation that is followed by the TKB-UO system at SemEval-2007. The underlying disambigua- tion method only uses WordNet as external resource, …...

In this paper we describe an unsuper- vised WordNet-based Word Sense Disam- biguation system, which participated (as UMND1) in the SemEval-2007 Coarse- grained English Lexical Sample task. The system disambiguates …...

One of the main challenges in the applica- tions (i.e.: text summarization, question an- swering, information retrieval, etc.) of Natural Language Processing is to deter- mine which of the several …...

We describe two systems participating of the English Lexical Sample task in SemEval- 2007. The systems make use of Inductive Logic Programming for supervised learning in two different ways: (a) …...

Supervised approaches to Word Sense Dis- ambiguation (WSD) have been shown to outperform other approaches but are ham- pered by reliance on labeled training ex- amples (the data acquisition bottleneck). …...

Human linguistic annotation is crucial for many natural language processing tasks but can be expensive and time-consuming. We ex- plore the use of Amazon’s Mechanical Turk system, a significantly cheaper …...

Some phrases can be interpreted either id- iomatically (figuratively) or literally in con- text, and the precise identification of idioms is indispensable for full-fledged natural lan- guage processing (NLP). To …...

Bootstrapping has a tendency, called seman- tic drift, to select instances unrelated to the seed instances as the iteration proceeds. We demonstrate the semantic drift of bootstrap- ping has the …...

AbstractTo create the first Hungarian WSD corpus, 39 suitable word form samples were selected for the purpose of word sense disambiguation. Among others, selection criteria required the given word form …...

This paper presents a new fully auto- matic method for building highly dense and accurate knowledge bases from ex- isting semantic resources. Basically, the method uses a wide-coverage and accu- …...

In this paper we explore robustness and domain adaptation issues for Word Sense Disambiguation (WSD) using Singular Value Decomposition (SVD) and unlabeled data. We focus on the semi-supervised do- main …...

Annotated corpora are only useful if their annotations are consistent. Most large-scale annotation efforts take special measures to reconcile inter-annotator disagreement. To date, however, no-one has investigated how to automatically …...

We explore the augmentation of statistical ma- chine translation models with features of the context of each phrase to be translated. This work extends several existing threads of re- search …...

AbstractThe paper describes the project held within Russian National Corpus (http://www.ruscorpora.ru). Beside such obligatory constituents of a linguistic corpus as POS (parts of speech) and morphological tagging RNC contains semantic …...

The accuracy of current word sense disam- biguation (WSD) systems is affected by the fine-grained sense inventory of WordNet as well as a lack of training examples. Using the WSD …...

Word sense disambiguation is the process of determining which sense of a word is used in a given context. Due to its importance in understanding semantics of natural languages, word …...

In the task of semantic category labeling, given a text, every word in it has to be assigned a semantic category. Our language of interest is Hindi. We use the …...

Word sense disambiguation is typically phrased as the task of labeling a word in context with the best-fitting sense from a sense inventory such as WordNet. While questions have often …...

The sense of a preposition is related to the semantics of its dominating prepositional phrase. Knowing the sense of a prepo- sition could help to correctly classify the semantic role …...

In this paper we propose a new graph- based method that uses the knowledge in a LKB (based on WordNet) in order to perform unsupervised Word Sense Disam- biguation. Our …...

We report in this paper a way of doing Word Sense Disambiguation (WSD) that has its ori- gin in multilingual MT and that is cognizant of the fact that parallel …...

In this paper, we discuss the importance of the quality against the quantity of automatically ex- tracted examples for word sense disambiguation (WSD). We first show that we can build …...

In this paper we propose a methodology for sen- timent analysis of figurative language which ap- plies Word Sense Disambiguation and, through an n-gram graph based method, assigns polar- ity …...

Word Sense Disambiguation is the task dedi- cated to the problem of finding out the sense of a word in context, from all of its many pos- sible senses. Solving …...

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

AbstractWe propose a methodology for a novel type of discourse annotation whose model is tuned to the analysis of a text as narrative. This is intended to be the basis …...

AbstractThis paper presents a word sense disambiguation (WSD) approach based on syntactic and logical representations. The objective here is to run a number of experiments to compare standard contexts (word …...

Word Sense Disambiguation remains one of the most complex problems facing com- putational linguists to date. In this pa- per we present a system that combines evidence from a monolingual …...

AbstractLexical substitution is the task of finding a replacement for a target word in a sentence so as to preserve, as closely as possible, the meaning of the original sentence. …...

AbstractIn this article, we present an experiment of linguistic parameter tuning in the representation of the semantic space of polysemous words. We evaluate quantitatively the influence of some basic linguistic …...

AbstractThe paper explores the co-reference chains as a way for improving the density of concept annotation over domain texts. The idea extends authors’ previous work on relating the ontology to …...

AbstractThis paper presents a novel automatic approach to partially integrate FrameNet and WordNet. In that way we expect to extend FrameNet coverage, to enrich WordNet with frame semantic information and …...

Like most natural language disambiguation tasks, word sense disambiguation (WSD) re- quires world knowledge for accurate predic- tions. Several proxies for this knowledge have been investigated, including labeled cor- pora, …...

This paper summarizes our participation in task #17 of SemEval–2 (All–words WSD on a specific domain) using a su- pervised class-based Word Sense Disam- biguation system. Basically, we use Sup- …...

This paper presents an unsupervised sys- tem for all-word domain specific word sense disambiguation task. This system tags target word with the most frequent sense which is estimated using a …...

AbstractWith the proliferation of applications sharing information represented in multiple ontologies, the development of automatic methods for robust and accurate ontology matching will be crucial to their success. Connecting and …...

Semantic role labeling (SRL) and word sense disambiguation (WSD) are two fun- damental tasks in natural language pro- cessing to find a sentence-level seman- tic representation. To date, they have …...

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

AbstractThis study attempts to pinpoint the factors that restrict reliable word sense annotation, focusing on the influence of the number of senses annotators use and the semantic granularity of those …...

AbstractIn the paper we investigate the impact of data size on a Word Sense Disambiguation task (WSD). We question the assumption that the knowledge acquisition bottleneck, which is known as …...

Word Sense Disambiguation (WSD) of- ten relies on a context model or vector constructed from the words that co-occur with the target word within the same text windows. In most …...

Word sense disambiguation (WSD) is one of the main challenges in Computational Linguistics. TreeMatch is a WSD system originally developed using data from SemEval 2007 Task 7 (Coarse-grained English All- …...

AbstractThis article presents a new freely available trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia and has been automatically enriched with linguistic information. To our knowledge, …...

Graph-based methods have gained attention in many areas of Natural Language Processing (NLP) including Word Sense Disambiguation (WSD), text summarization, keyword extrac- tion and others. Most of the work in …...

We explore the relation between word sense subjectivity and cross-lingual lexical substitu- tion, following the intuition that good substi- tutions will transfer a word’s (contextual) sen- timent from the source …...

In this paper, we introduce a hybrid method to associate English collocations with sense class members chosen from WordNet. Our combinational approach includes a learning-based method, a paraphrase-based method and …...

Domain adaptation (DA), which involves adapting a classifier developed from source to target data, has been studied intensively in re- cent years. However, when DA for word sense disambiguation (WSD) …...

In this paper we introduce a method for searching appropriate articles from knowledge bases (e.g. Wikipedia) for a given query and its context. In our approach, this problem is transformed …...

AbstractThe DBpedia project extracts structured information from Wikipedia editions in 97 different languages and combines this information into a large multi-lingual knowledge base covering many specific domains and general world …...

AbstractWord Sense Disambiguation (WSD) systems require large sense-tagged corpora along with lexical databases to reach satisfactory results. The number of English language resources for developed WSD increased in the past …...

AbstractCollocations can be defined as words that occur together significantly more often than it would be expected by chance. Many natural language processing applications such as natural language generation, word …...

AbstractThis paper presents deepKnowNet, a new fully automatic method for building highly dense and accurate knowledge bases from existing semantic resources. Basically, the method applies a knowledge-based Word Sense Disambiguation …...

AbstractThe aim of this paper is to present a system for semantic text annotation called Inforex. Inforex is a web-based system designed for managing and annotating text corpora on the …...

We present a novel approach for jointly disambiguating and clustering known and unknown concepts and entities with Markov Logic. Concept and entity disambiguation is the task of identifying the correct …...

AbstractThis paper presents SentiSense, a concept-based affective lexicon. It is intended to be used in sentiment analysis-related tasks, specially in polarity and intensity classification and emotion identification. SentiSense attaches emotional …...

This paper briefly reports our submissions to the Semantic Textual Similarity (STS) task in the SemEval 2012 (Task 6). We first use knowledge-based methods to compute word semantic similarity as …...

We propose a new measure of semantic similarity between words in context, which exploits the syntactic/semantic structure of the context surrounding each target word. For a given pair of target …...

We introduce Peripheral Diversity (PD) as a knowledge-based approach to achieve multi- lingual Word Sense Disambiguation (WSD). PD exploits the frequency and diverse use of word senses in semantic subgraphs …...

This paper proposes a novel smoothing model with a combinatorial optimization scheme for all-words word sense disam- biguation from untagged corpora. By gen- eralizing discrete senses to a continuum, we …...

. In this article, we present the notions of local and global algorithms, for the word sense disambiguation of texts. A local algorithm allows to calculate the semantic similarity between …...

Word sense disambiguation aims to identify which meaning of a word is present in a given usage. Gathering word sense annotations is a laborious and difficult task. Several methods have …...

Word Sense Disambiguation (WSD) ap- proaches have reported good accuracies in recent years. However, these approaches can be classified as weak AI systems. According to the classical definition, a strong …...

Most compositional-distributional models of meaning are based on ambiguous vec- tor representations, where all the senses of a word are fused into the same vec- tor. This paper provides evidence …...

The topic of a document can prove to be use- ful information for Word Sense Disambigua- tion (WSD) since certain meanings tend to be associated with particular topics. This paper …...

Topic models can be used in an unsuper- vised domain adaptation for Word Sense Disambiguation (WSD). In the domain adaptation task, three types of topic mod- els are available: (1) …...

WoNeF, an improved, extended and evaluated automatic French translation of WordNet Identifying the various possible meanings of each word of the vocabulary is a difficult problem that requires a lot …...

The “one sense per discourse” (OSPD) and “one sense per collocation” (OSPC) hypotheses have been very influential in Word Sense Disambiguation. The goal of this paper is twofold: (i) to …...

Most word representation methods assume that each word owns a single semantic vec- tor. This is usually problematic because lexical ambiguity is ubiquitous, which is also the problem to be …...

This paper describes a new Word Sense Disambiguation (WSD) algorithm which extends two well-known variations of the Lesk WSD method. Given a word and its context, Lesk algorithm exploits the …...

. We address the problem of transferring semantic annotations, more specifically predicate labellings, from one language to another using parallel corpora. Previous work has transferred these annotations directly at the …...

AbstractEntity Linking (EL) and Word Sense Disambiguation (WSD) both address the lexical ambiguity of language. But while the two tasks are pretty similar, they differ in a fundamental respect: in …...

AbstractAnnotated data is prerequisite for many NLP applications. Acquiring large-scale annotated corpora is a major bottleneck, requiring significant time and resources. Recent work has proposed turning annotation into a game …...

AbstractThis article describes a lexical substitution dataset for German. The whole dataset contains 2,040 sentences from the German Wikipedia, with one target word in each sentence. There are 51 target …...

AbstractIn this paper we present the mapping between WordNet domains and WordNet topics, and the emergent Wikipedia categories. This mapping leads to a coarse alignment between WordNet and Wikipedia, useful …...

The identification of cognates between two distinct languages has recently start- ed to attract the attention of NLP re- search, but there has been little research into using semantic evidence …...

In this paper we present new research in translation assistance. We describe a sys- tem capable of translating native language (L1) fragments to foreign language (L2) fragments in an L2 …...

This paper describes the UNIBA team participation in the Cross-Level Semantic Similarity task at SemEval 2014. We pro- pose to combine the output of different se- mantic similarity measures which …...

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

AbstractThe coverage and quality of conceptual information contained in lexical semantic resources is crucial for many tasks in natural language processing. Automatic alignment of complementary resources is one way of …...

A range of approaches to the representa- tion of lexical semantics have been explored within Computational Linguistics. Two of the most popular are distributional and knowledge- based models. This paper …...

Word Sense Disambiguation has been stuck for many years. In this paper we explore the use of large-scale crowdsourcing to cluster senses that are often confused by non-expert annotators. We …...

We introduce an approach to word sense dis- ambiguation and entity linking that combines a set of complementary objectives in an exten- sible multi-objective formalism. During dis- ambiguation the system …...

In this paper, we propose the use of word sense disambiguation and latent semantic features to automatically identify a person’s perspective from his/her written text. We run an Ama- zon …...

In this paper we present the Multilingual All- Words Sense Disambiguation and Entity Link- ing task. Word Sense Disambiguation (WSD) and Entity Linking (EL) are well-known prob- lems in the …...

One of the weaknesses of current supervised word sense disambiguation (WSD) systems is that they only treat a word as a discrete en- tity. However, a continuous-space represen- tation of …...

An acid test for any new Word Sense Disam- biguation (WSD) algorithm is its performance against the Most Frequent Sense (MFS). The field of WSD has found the MFS baseline …...

In this paper we present an unsupervised ap- proach to word sense disambiguation based on evolutionary game theory. In our algorithm each word to be disambiguated is represented as a …...

AbstractNamed Entity Disambiguation (NED) is the task of linking a named-entity mention to an instance in a knowledge-base, typically Wikipedia-derived resources like DBpedia. This task is closely related to word-sense …...

AbstractWord Sense Disambiguation (WSD) systems tend to have a strong bias towards assigning the Most Frequent Sense (MFS), which results in high performance on the MFS but in a very …...

AbstractAmong all researches dedicating to terminology and word sense disambiguation, little attention has been devoted to the ambiguity of term occurrences. If a lexical unit is indeed a term of …...

Word embedding has been widely studied and proven helpful in solving many natural lan- guage processing tasks. However, the ambi- guity of natural language is always a prob- lem on …...

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

Recent years have seen a dramatic growth in the popularity of word embeddings mainly owing to their ability to capture se- mantic information from massive amounts of textual content. As …...

AbstractThere is a rich flora of word space models that have proven their efficiency in many different applications including information retrieval (Dumais, 1988), word sense disambiguation (Schutze, 1992), various semantic …...

We describe the implementation of a Word Sense Disambiguation (WSD) tool in a Dutch Text-to-Pictograph translation sys- tem, which converts textual messages into sequences of pictographic images. The system is …...

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

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

AbstractThis article presents a new model for word sense disambiguation formulated in terms of evolutionary game theory, where each word to be disambiguated is represented as a node on a …...

Word sense disambiguation is necessary in translation because different word senses often have different translations. Neural machine translation models learn differ- ent senses of words as part of an end- …...

AbstractIn this paper, we propose using metaheuristics—in particular, simulated annealing and the new D-Bees algorithm—to solve word sense disambiguation as an optimization problem within a knowledge-based lexical substitution system. We …...

AbstractWord Sense Disambiguation models exist in many flavors. Even though supervised ones tend to perform best in terms of accuracy, they often lose ground to more flexible knowledge-based solutions, which …...

Statistical machine translation (SMT) sys- tems use local cues from n-gram trans- lation and language models to select the translation of each source word. Such systems do not explicitly perform …...

AbstractAnnotating large numbers of sentences with senses is the heaviest requirement of current Word Sense Disambiguation. We present Train-O-Matic, a language-independent method for generating millions of sense-annotated training instances for …...

AbstractWe propose to improve word sense embeddings by enriching an automatic corpus-based method with lexicographic data. Information from a lexicon is introduced into the learning algorithm’s objective function through a …...

AbstractThe current trend in NLP is the use of highly opaque models, e.g. neural networks and word embeddings. While these models yield state-of-the-art results on a range of tasks, their …...

AbstractWord Sense Disambiguation is a long-standing task in Natural Language Processing, lying at the core of human language understanding. However, the evaluation of automatic systems has been problematic, mainly due …...

AbstractLSTM-based language models have been shown effective in Word Sense Disambiguation (WSD). In particular, the technique proposed by Yuan et al. (2016) returned state-of-the-art performance in several benchmarks, but neither …...

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, …...

AbstractRecent work has shown that the encoder-decoder attention mechanisms in neural machine translation (NMT) are different from the word alignment in statistical machine translation. In this paper, we focus on …...

In this paper, we present Watasense, an unsupervised system for word sense disambiguation. Given a sentence, the system chooses the most relevant sense of each input word with respect to …...

. In Word Sense Disambiguation, there are still few usages of neural networks. This direction is very promising however, the results obtained by these first systems being systemat- ically in …...

Supervised models for Word Sense Disambiguation (WSD) currently yield to state-of-the-art results in the most popular benchmarks. Despite the recent...

Neural machine translation systems have been shown to achieve state-of-the-art translation per- formance for many language pairs. In order to produce a correct translation, MT systems must learn how to …...

The task of Word Sense Disambiguation (WSD) is to determine the meaning of an ambiguous word in a given context. In spite of its importance for most NLP pipelines, WSD …...

AbstractHomographs, words with different meanings but the same surface form, have long caused difficulty for machine translation systems, as it is difficult to select the correct translation based on the …...

AbstractThis paper demonstrates that word sense disambiguation (WSD) can improve neural machine translation (NMT) by widening the source context considered when modeling the senses of potentially ambiguous words. We first …...

AbstractWord composition is a promising technique for representation learning of large linguistic units (e.g., phrases, sentences and documents). However, most of the current composition models do not take the ambiguity …...

AbstractThis paper describes CzEngClass, a bilingual lexical resource being built to investigate verbal synonymy in bilingual context and to relate semantic roles common to one synonym class to verb arguments …...

In this study, we investigated unsupervised learning based Korean word sense disambiguation (WSD) using CoreNet, a Korean lexical semantic network. To facilitate the application of WSD to practical natural language …...

AbstractWord sense disambiguation (WSD) is the task to determine the word sense according to its context. Many existing WSD studies have been using an external knowledge-based unsupervised approach because it …...

WordNet-like resources are lexical databases with highly relevance information and data which could be exploited in more complex computational linguistics research and applications. The building process requires manual and automatic …...

AbstractWord Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context. Traditional supervised methods rarely take into consideration the lexical resources like WordNet, …...

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

AbstractContextual embeddings represent a new generation of semantic representations learned from Neural Language Modelling (NLM) that addresses the issue of meaning conflation hampering traditional word embeddings. In this work, we …...

AbstractGraph measures, such as node distances, are inefficient to compute. We explore dense vector representations as an effective way to approximate the same information. We introduce a simple yet efficient …...

AbstractWord Sense Disambiguation remains a challenging NLP task. Due to the lack of annotated training data, especially for rare senses, the supervised approaches are usually designed for specific subdomains limited …...

AbstractWe propose a novel generative model to explore both local and global context for joint learning topics and topic-specific word embeddings. In particular, we assume that global latent topics are …...

AbstractContextual embeddings are proved to be overwhelmingly effective to the task of Word Sense Disambiguation (WSD) compared with other sense representation techniques. However, these embeddings fail to embed sense knowledge …...

AbstractDomain adaptation or transfer learning using pre-trained language models such as BERT has proven to be an effective approach for many natural language processing tasks. In this work, we propose …...

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

AbstractLexical ambiguity is one of the many challenging linguistic phenomena involved in translation, i.e., translating an ambiguous word with its correct sense. In this respect, previous work has shown that …...

AbstractKnowing the Most Frequent Sense (MFS) of a word has been proved to help Word Sense Disambiguation (WSD) models significantly. However, the scarcity of sense-annotated data makes it difficult to …...

AbstractTo date, the most successful word, word sense, and concept modelling techniques have used large corpora and knowledge resources to produce dense vector representations that capture semantic similarities in a …...

AbstractWord sense disambiguation is a well-known source of translation errors in NMT. We posit that some of the incorrect disambiguation choices are due to models’ over-reliance on dataset artifacts found …...

AbstractA major obstacle in Word Sense Disambiguation (WSD) is that word senses are not uniformly distributed, causing existing models to generally perform poorly on senses that are either rare or …...

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 …...

AbstractThe knowledge acquisition bottleneck problem dramatically hampers the creation of sense-annotated data for Word Sense Disambiguation (WSD). Sense-annotated data are scarce for English and almost absent for other languages. This …...

AbstractDisambiguation of word senses in context is easy for humans, but is a major challenge for automatic approaches. Sophisticated supervised and knowledge-based models were developed to solve this task. However, …...

AbstractWord Sense Disambiguation (WSD) aims to automatically identify the exact meaning of one word according to its context. Existing supervised models struggle to make correct predictions on rare word senses …...

AbstractSupervised systems have nowadays become the standard recipe for Word Sense Disambiguation (WSD), with Transformer-based language models as their primary ingredient. However, while these systems have certainly attained unprecedented performances, …...

AbstractCurrent models for Word Sense Disambiguation (WSD) struggle to disambiguate rare senses, despite reaching human performance on global WSD metrics. This stems from a lack of data for both modeling …...

AbstractThis paper describes our submission to SemEval-2021 Task 1: predicting the complexity score for single words. Our model leverages standard morphosyntactic and frequency-based features that proved helpful for Complex Word …...

Contextualised word embeddings generated from Neural Language Models (NLMs), such as BERT, represent a word with a vector that considers the semantics of the target word as well its context. …...

AbstractA critical challenge faced by supervised word sense disambiguation (WSD) is the lack of large annotated datasets with sufficient coverage of words in their diversity of senses. This inspired recent …...

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 …...

AbstractMost words are ambiguous—-i.e., they convey distinct meanings in different contexts—-and even the meanings of unambiguous words are context-dependent. Both phenomena present a challenge for NLP. Recently, the advent of …...

AbstractAcquisition of multilingual training data continues to be a challenge in word sense disambiguation (WSD). To address this problem, unsupervised approaches have been proposed to automatically generate sense annotations for …...

AbstractLately proposed Word Sense Disambiguation (WSD) systems have approached the estimated upper bound of the task on standard evaluation benchmarks. However, these systems typically implement the disambiguation of words in …...

Existing knowledge graph-based learning methods for word sense disambiguation use word-to-word relations to learn models, but do not learn using the hierarchical relations of word senses in a single word. …...

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. …...

Most Word-Sense Disambiguation (WSD) systems rely on machine learning approaches that require large-scale corpora for effective training. So, the quality of a WSD system degrades when trained in a low-resource …...

AbstractLinguists distinguish between novel and conventional metaphor, a distinction which the metaphor detection task in NLP does not take into account. Instead, metaphoricity is formulated as a property of a …...

AbstractWord sense disambiguation (WSD) is a crucial problem in the natural language processing (NLP) community. Current methods achieve decent performance by utilizing supervised learning and large pre-trained language models. However, …...

AbstractThe disambiguation of causative-passive homonymy (CPH) is potentially tricky for machines, as the causative and the passive are not distinguished by the sentences’ syntactic structure. By transforming CPH disambiguation to …...

AbstractCross-lingual word embeddings (CLWE) have been proven useful in many cross-lingual tasks. However, most existing approaches to learn CLWE including the ones with contextual embeddings are sense agnostic. In this …...

AbstractIn this paper, we present the Universal Semantic Annotator (USeA), which offers the first unified API for high-quality automatic annotations of texts in 100 languages through state-of-the-art systems for Word …...

AbstractWord sense disambiguation (WSD), identifying the most suitable meaning of ambiguous words in the given contexts according to a predefined sense inventory, is one of the most classical and challenging …...

AbstractSuccessful word sense disambiguation (WSD)is a fundamental element of natural languageunderstanding. As part of SemEval-2023 Task1, we investigate WSD in a multimodal setting,where ambiguous words are to be matched withcandidate …...

AbstractA promising approach for knowledge-based Word Sense Disambiguation (WSD) is to select the sense whose contextualized embeddings computed for its definition sentence are closest to those computed for a target …...

AbstractPrompt tuning is an efficient method for adapting large language models, and Soft Prompt Transfer (SPoT) further narrows the gap between prompt tuning and full model tuning by transferring prompts …...

AbstractIn SemEval-2023 Task 1, a task of applying Word Sense Disambiguation in an image retrieval system was introduced. To resolve this task, this work proposes three approaches: (1) an unsupervised …...

AbstractHumans often make creative use of words to expressnovel senses. A long-standing effort in natural language processing hasbeen focusing on word sense disambiguation (WSD), but little has been explored about …...