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Named entity recognition for Chinese electronic medical records by integrating knowledge graph and ClinicalBERT. [PDF]
Front Artif IntellXu X, Li Z, Zhang H, Ma K.
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ACM Computing Surveys, 2009
Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence.
Roberto Navigli
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Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence.
Roberto Navigli
exaly +3 more sources
Trends in word sense disambiguation
Artificial Intelligence Review, 2012The problem and process of identifying the meaning of a word as per its usage context is called word sense disambiguation (WSD). Although research in this field has been ongoing for the past forty years, a distinct change of techniques adopted can be observed over time.
S Abirami
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Word sense disambiguation for Turkish
2009 24th International Symposium on Computer and Information Sciences, 2009Word Sense Disambiguation (WSD) is the core and one of the hardest problems of many Natural Language Processing tasks. WSD is considered as an AI-complete problem. Although there are many approaches trying to solve this problem, many of them are not adequate to solve WSD problem for Turkish.
Ezgi Mert, Gökhan Dalkiliç
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Sense Space for Word Sense Disambiguation
2018 IEEE International Conference on Big Data and Smart Computing (BigComp), 2018Word sense disambiguation is essential for semantic analysis in many natural language-related applications, such as information retrieval, data mining, and machine translation. One of the effective models for word sense disambiguation is the word space model that represents context vectors and sense vectors in a word vector space.
Myung Yun Kang +2 more
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2013
This chapter discusses the basic concepts of Word Sense Disambiguation (WSD) and the approaches to solving this problem. Both general purpose WSD and domain specific WSD are presented. The first part of the discussion focuses on existing approaches for WSD, including knowledge-based, supervised, semi-supervised, unsupervised, hybrid, and bilingual ...
Pushpak Bhattacharyya, Mitesh Khapra
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This chapter discusses the basic concepts of Word Sense Disambiguation (WSD) and the approaches to solving this problem. Both general purpose WSD and domain specific WSD are presented. The first part of the discussion focuses on existing approaches for WSD, including knowledge-based, supervised, semi-supervised, unsupervised, hybrid, and bilingual ...
Pushpak Bhattacharyya, Mitesh Khapra
+5 more sources
Word sense disambiguation methods
Programming and Computer Software, 2010Word sense disambiguation is one of the key tasks of text processing. It consists in the determination of senses of words or compound terms in accordance with the context where they were used. The word sense disambiguation problem originated in the 1950s as a subtask of machine translation.
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Multiwords and Word Sense Disambiguation
2005This paper studies the impact of multiword expressions on Word Sense Disambiguation (WSD). Several identification strategies of the multiwords in WordNet2.0 are tested in a real Senseval-3 task: the disambiguation of WordNet glosses. Although we have focused on Word Sense Disambiguation, the same techniques could be applied in more complex tasks, such ...
Victoria Arranz +2 more
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