Results 41 to 50 of about 83,244 (255)
Word sense disambiguation in queries [PDF]
Proceedings of the 14th ACM international conference on Information and knowledge management, 2005 This paper presents a new approach to determine the senses of words in queries by using WordNet. In our approach, noun phrases in a query are determined first. For each word in the query, information associated with it, including its synonyms, hyponyms, hypernyms, definitions of its synonyms and hyponyms, and its domains, can be used for word sense ...
Shuang Liu, Clement Yu, Weiyi Meng
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Graph Convolutional Network for Word Sense Disambiguation
Discrete Dynamics in Nature and Society, 2021 Word sense disambiguation (WSD) is an important research topic in natural language processing, which is widely applied to text classification, machine translation, and information retrieval.
Chun-Xiang Zhang +3 more
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Fast max-margin clustering for unsupervised word sense disambiguation in biomedical texts [PDF]
BMC Bioinformatics, 2009 Weisi Duan, Min Song, Alexander Yates
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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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Hybrid Sense Classification Method for Large-Scale Word Sense Disambiguation
IEEE Access, 2020 Word sense disambiguation (WSD) is a task of determining a reasonable sense of a word in a particular context. Although recent studies have demonstrated some progress in the advancement of neural language models, the scope of research is still such that ...
Yoonseok Heo, Sangwoo Kang, Jungyun Seo
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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
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Conference on Empirical Methods in Natural Language Processing, 2020 Contextualized word embeddings have been employed effectively across several tasks in Natural Language Processing, as they have proved to carry useful semantic information. However, it is still hard to link them to structured sources of knowledge.
Bianca Scarlini +2 more
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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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Complexity, 2020 In natural language, the phenomenon of polysemy is widespread, which makes it very difficult for machines to process natural language. Word sense disambiguation is a key issue in the field of natural language processing.
Lei Wang, Qun Ai
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Word sense disambiguation with pictures
Artificial Intelligence, 2005 AbstractWe introduce using images for word sense disambiguation, either alone, or in conjunction with traditional text based methods. The approach is based on a recently developed method for automatically annotating images by using a statistical model for the joint probability for image regions and words. The model itself is learned from a data base of
JohnsonMatthew, BarnardKobus
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