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Word Sense Disambiguation [PDF]
, 2012 Word-sense disambiguation (WSD) is the process of identifying the meanings of words in context. This article begins with discussing the origins of the problem in the earliest machine translation systems. Early attempts to solve the WSD problem suffered from a lack of coverage.
Mark Stevenson, Yorick Wilks
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Chinese Word Sense Disambiguation Based on Word translation and Part of speech
Journal of Harbin University of Science and Technology, 2020 For vocabulary ambiguity problem in Chinese, CNN (Convolution Neural Network) is adopted to determine true meaning of ambiguous vocabulary where word, part of speech and translation around its left and right adjacent words are used.
ZHANG Chunxiang +2 more
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Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration
Conference of the European Chapter of the Association for Computational Linguistics, 2021 Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem in which one is asked to choose only the best-fitting sense for a target word, given its context.
Simone Conia, Roberto Navigli
semanticscholar +1 more source
SBU-WSD-Corpus: A Sense Annotated Corpus for Persian All-words Word Sense Disambiguation [PDF]
International Journal of Web Research, 2022 Word Sense Disambiguation (WSD) is a long standing task in Natural Language Processing (NLP) that aims to automatically identify the most relevant meaning of the words in a given context.
Hossein Rouhizadeh +2 more
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Nibbling at the Hard Core of Word Sense Disambiguation
Annual Meeting of the Association for Computational Linguistics, 2022 With state-of-the-art systems having finally attained estimated human performance, Word Sense Disambiguation (WSD) has now joined the array of Natural Language Processing tasks that have seemingly been solved, thanks to the vast amounts of knowledge ...
Marco Maru +3 more
semanticscholar +1 more source
Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation [PDF]
Annual Meeting of the Association for Computational Linguistics, 2019 Contextual 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.
Daniel Loureiro, A. Jorge
semanticscholar +1 more source
Transfer Learning and Augmentation for Word Sense Disambiguation [PDF]
European Conference on Information Retrieval, 2021 Many downstream NLP tasks have shown significant improvement through continual pre-training, transfer learning and multi-task learning. State-of-the-art approaches in Word Sense Disambiguation today benefit from some of these approaches in conjunction ...
Harsh Kohli
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Unsupervised Word Sense Disambiguation Rivaling Supervised Methods
Annual Meeting of the Association for Computational Linguistics, 1995 This paper presents an unsupervised learning algorithm for sense disambiguation that, when trained on unannotated English text, rivals the performance of supervised techniques that require time-consuming hand annotations.
David Yarowsky
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Annual Meeting of the Association for Computational Linguistics, 2020 Neural architectures are the current state of the art in Word Sense Disambiguation (WSD). However, they make limited use of the vast amount of relational information encoded in Lexical Knowledge Bases (LKB).
Michele Bevilacqua, Roberto Navigli
semanticscholar +1 more source
International Conference on Computational Logic, 2021 Nowadays, most research conducted in the field of abstractive text summarization focuses on neural-based models alone, without considering their combination with knowledge-based approaches that could further enhance their efficiency.
P. Kouris +2 more
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