Results 81 to 90 of about 84,634 (222)
CSI: A Coarse Sense Inventory for 85% Word Sense Disambiguation
AAAI Conference on Artificial Intelligence, 2020 Word Sense Disambiguation (WSD) is the task of associating a word in context with one of its meanings. While many works in the past have focused on raising the state of the art, none has even come close to achieving an F-score in the 80% ballpark when ...
Caterina Lacerra +3 more
semanticscholar +1 more source
Word Sense Disambiguation with THESSOM [PDF]
, 2003 Word sense disambiguation automatically determines the appropriate senses of a word in context. We have previously shown that self-organized document maps have properties similar to a large-scale semantic structure that is useful for word sense ...
Linden, Krister
core
Word sense disambiguation: a complex network approach
, 2018 In recent years, concepts and methods of complex networks have been employed to tackle the word sense disambiguation (WSD) task by representing words as nodes, which are connected if they are semantically similar.
Amancio, Diego R. +2 more
core +1 more source
Word Sense Disambiguation by Web Mining for Word Co-occurrence Probabilities [PDF]
, 2004 This paper describes the National Research Council (NRC) Word Sense Disambiguation (WSD) system, as applied to the English Lexical Sample (ELS) task in Senseval-3.
Turney, Peter D.
core +2 more sources
Normalized Statistical Algorithm for Afaan Oromo Word Sense Disambiguation [PDF]
, 2021 Abdo Ababor Abafogi
openalex +1 more source
MuLaN: Multilingual Label propagatioN for Word Sense Disambiguation
International Joint Conference on Artificial Intelligence, 2020 The knowledge acquisition bottleneck strongly affects the creation of multilingual sense-annotated data, hence limiting the power of supervised systems when applied to multilingual Word Sense Disambiguation.
Edoardo Barba +4 more
semanticscholar +1 more source
A Synset Relation-enhanced Framework with a Try-again Mechanism for Word Sense Disambiguation
Conference on Empirical Methods in Natural Language Processing, 2020 Contextual embeddings are proved to be overwhelmingly effective to the task of Word Sense Disambiguation (WSD) compared with other sense representation techniques. However, these embeddings fail to embed sense knowledge in semantic networks.
Ming Wang, Yinglin Wang
semanticscholar +1 more source
Try to Substitute: An Unsupervised Chinese Word Sense Disambiguation Method Based on HowNet
International Conference on Computational Linguistics, 2020 Word sense disambiguation (WSD) is a fundamental natural language processing task. Unsupervised knowledge-based WSD only relies on a lexical knowledge base as the sense inventory and has wider practical use than supervised WSD that requires a mass of ...
Bairu Hou +5 more
semanticscholar +1 more source
SensPick: Sense Picking for Word Sense Disambiguation [PDF]
, 2021 SM Zobaed +3 more
openalex +1 more source
Sense Unveiled: Enhancing Urdu Corpus for Nuanced Word Sense Disambiguation
IEEE AccessAmbiguity in word meanings presents a significant challenge in natural language processing, necessitating robust techniques for Word Sense Disambiguation (WSD).
Sarfraz Bibi +2 more
doaj +1 more source