Results 41 to 50 of about 29,472 (315)
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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Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation [PDF]
, 2017 Interpretability of a predictive model is a powerful feature that gains the trust of users in the correctness of the predictions. In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpretable than knowledge-free ...
Biemann, Chris +6 more
core +3 more sources
John Cook Wilson on the indefinability of knowledge
European Journal of Philosophy, Volume 30, Issue 4, Page 1547-1564, December 2022., 2022 Abstract Can knowledge be defined? We expound an argument of John Cook Wilson's that it cannot. Cook Wilson's argument connects knowing with having the power to inquire. We suggest that if he is right about that connection, then knowledge is, indeed, indefinable.
Guy Longworth, Simon Wimmer
wiley +1 more source
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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Morphological Disambiguation from Stemming Data [PDF]
, 2020 Morphological analysis and disambiguation is an important task and a crucial preprocessing step in natural language processing of morphologically rich languages. Kinyarwanda, a morphologically rich language, currently lacks tools for automated morphological analysis.
arxiv +1 more source
Word sense disambiguation with pictures [PDF]
Proceedings of the HLT-NAACL 2003 workshop on Learning word meaning from non-linguistic data -, 2003 We introduce a method for using images for word sense disambiguation, either alone, or in conjunction with traditional text based methods. The approach is based in recent work on a method for predicting words for images which can be learned from image datasets with associated text.
David Forsyth +2 more
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Nibbling at the Hard Core of Word Sense Disambiguation
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 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 encoded into Transformer-based pre-trained language models. And yet, if we look below the surface of raw
Maru, Marco +3 more
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Word sense disambiguation and information retrieval [PDF]
, 1994 It has often been thought that word sense ambiguity is a cause of poor performance in Information Retrieval (IR) systems. The belief is that if ambiguous words can be correctly disambiguated, IR performance will increase.
Sanderson, M.
core +1 more source
Unsupervised Word Sense Disambiguation Using Word Embeddings [PDF]
Proceedings of the XXth Conference of Open Innovations Association FRUCT, 2019 Word sense disambiguation is the task of assigning the correct sense of a polysemous word in the context in which it appears. In recent years, word embeddings have been applied successfully to many NLP tasks.
Behzad Moradi +2 more
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