Contextual SenSe Model: Word Sense Disambiguation Using Sense and Sense Value of Context Surrounding the Target

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Sense Disambiguation Using Sense and Sense Value of Context Surrounding the Target</title> <meta name="description" content="Contextual SenSe Model: Word Sense Disambiguation Using Sense and Sense Value of Context Surrounding the Target"> <meta name="keywords" content="Word Sense Disambiguation, WSD, Contextual SenSe Model, Most Frequent Sense, part of speech, POS, Natural Language Processing, NLP, OOV, out of vocabulary, ELMo, Embeddings from Language Model, BERT, Bidirectional Encoder Representations from Transformers, Word2Vec, lemma_POS, Algorithm."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="Contextual SenSe Model: Word Sense Disambiguation Using Sense and Sense Value of Context Surrounding the Target"> <meta name="citation_author" content="Vishal Raj"> <meta name="citation_author" content="Noorhan Abbas"> <meta name="citation_publication_date" 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Sense Disambiguation Using Sense and Sense Value of Context Surrounding the Target</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Vishal%20Raj">Vishal Raj</a>, <a href="https://publications.waset.org/search?q=Noorhan%20Abbas"> Noorhan Abbas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Ambiguity in NLP (Natural Language Processing) refers to the ability of a word, phrase, sentence, or text to have multiple meanings. This results in various kinds of ambiguities such as lexical, syntactic, semantic, anaphoric and referential. This study is focused mainly on solving the issue of Lexical ambiguity. Word Sense Disambiguation (WSD) is an NLP technique that aims to resolve lexical ambiguity by determining the correct meaning of a word within a given context. Most WSD solutions rely on words for training and testing, but we have used lemma and Part of Speech (POS) tokens of words for training and testing. Lemma adds generality and POS adds properties of word into token. We have designed a method to create an affinity matrix to calculate the affinity between any pair of lemma_POS (a token where lemma and POS of word are joined by underscore) of given training set. Additionally, we have devised an algorithm to create the sense clusters of tokens using affinity matrix under hierarchy of POS of lemma. Furthermore, three different mechanisms to predict the sense of target word using the affinity/similarity value are devised. Each contextual token contributes to the sense of target word with some value and whichever sense gets higher value becomes the sense of target word. So, contextual tokens play a key role in creating sense clusters and predicting the sense of target word, hence, the model is named Contextual SenSe Model (CSM). CSM exhibits a noteworthy simplicity and explication lucidity in contrast to contemporary deep learning models characterized by intricacy, time-intensive processes, and challenging explication. CSM is trained on SemCor training data and evaluated on SemEval test dataset. The results indicate that despite the naivety of the method, it achieves promising results when compared to the Most Frequent Sense (MFS) model. </p> <iframe src="https://publications.waset.org/10013458.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Word%20Sense%20Disambiguation" title="Word Sense Disambiguation">Word Sense Disambiguation</a>, <a href="https://publications.waset.org/search?q=WSD" title=" WSD"> WSD</a>, <a href="https://publications.waset.org/search?q=Contextual%20SenSe%20Model" title=" Contextual SenSe Model"> Contextual SenSe Model</a>, <a href="https://publications.waset.org/search?q=Most%20Frequent%20Sense" title=" Most Frequent Sense"> Most Frequent Sense</a>, <a href="https://publications.waset.org/search?q=part%20of%20speech" title=" part of speech"> part of speech</a>, <a href="https://publications.waset.org/search?q=POS" title=" POS"> POS</a>, <a href="https://publications.waset.org/search?q=Natural%20Language%20Processing" title=" Natural Language Processing"> Natural Language Processing</a>, <a href="https://publications.waset.org/search?q=NLP" title=" NLP"> NLP</a>, <a href="https://publications.waset.org/search?q=OOV" title=" OOV"> OOV</a>, <a href="https://publications.waset.org/search?q=out%20of%20vocabulary" title=" out of vocabulary"> out of vocabulary</a>, <a href="https://publications.waset.org/search?q=ELMo" title=" ELMo"> ELMo</a>, <a href="https://publications.waset.org/search?q=Embeddings%20from%20Language%20Model" title=" Embeddings from Language Model"> Embeddings from Language Model</a>, <a href="https://publications.waset.org/search?q=BERT" title=" BERT"> BERT</a>, <a href="https://publications.waset.org/search?q=Bidirectional%20Encoder%20Representations%20from%20Transformers" title=" Bidirectional Encoder Representations from Transformers"> Bidirectional Encoder Representations from Transformers</a>, <a href="https://publications.waset.org/search?q=Word2Vec" title=" Word2Vec"> Word2Vec</a>, <a href="https://publications.waset.org/search?q=lemma_POS" title=" lemma_POS"> lemma_POS</a>, <a href="https://publications.waset.org/search?q=Algorithm." title=" Algorithm."> Algorithm.</a> </p> <a href="https://publications.waset.org/10013458/contextual-sense-model-word-sense-disambiguation-using-sense-and-sense-value-of-context-surrounding-the-target" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013458/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013458/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013458/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013458/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013458/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013458/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013458/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013458/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013458/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013458/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013458.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">387</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] Zhi Zhong and Hwee Tou Ng. 2012. 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Context clustering for Word Sense Disambiguation based on modeling pairwise context similarities. Proceedings of SENSEVAL-3, the Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (ACL), pages 187–190. <br>[16] Nameh, M. S., Fakhrahmad, M., Jahromi, M.Z. 2011. A New Approach to Word Sense Disambiguation Based on Context Similarity. Proceedings of the World Congress on Engineering, Vol. 1, pages 456-459. <br>[17] Mikolov, Tomas & Chen, Kai & Corrado, G.s & Dean, Jeffrey. 2013. Efficient Estimation of Word Representations in Vector Space. Proceedings of Workshop at ICLR. 2013, arXiv preprint arXiv:1301.3781. <br>[18] Pennington, J., Socher, R., Manning, C.D., 2014. ‘‘Glove: Global vectors for word representation,” in Empirical Methods in Natural Language Processing, pages 1532–1543. <br>[19] Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep Contextualized Word Representations. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol. 1, pages 2227–2237. <br>[20] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol. 1, pages 4171–4186. <br>[21] Alessandro Raganato, Jose Camacho-Collados, and Roberto Navigli. 2017. Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Vol. 1, pages 99–110. <br>[22] Rami Al-Rfou’, Bryan Perozzi, and Steven Skiena. 2013. Polyglot: Distributed Word Representations for Multilingual NLP. 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Contextual SenSe Model: Word Sense Disambiguation Using Sense and Sense Value of Context Surrounding the Target