Results 21 to 30 of about 82,228 (264)
Multilingual Word Sense Disambiguation with Unified Sense Representation [PDF]
International Conference on Computational Linguistics, 2022 As a key natural language processing (NLP) task, word sense disambiguation (WSD) evaluates how well NLP models can understand the fine-grained semantics of words under specific contexts. Benefited from the large-scale annotation, current WSD systems have
Ying Su +3 more
semanticscholar +1 more source
SemEval-2023 Task 1: Visual Word Sense Disambiguation
International Workshop on Semantic Evaluation, 2023 This paper presents the Visual Word Sense Disambiguation (Visual-WSD) task.The objective of Visual-WSD is to identify among a set of ten images the one that corresponds to the intended meaning of a given ambiguous word which is accompanied with minimal ...
Alessandro Raganato +4 more
semanticscholar +1 more source
Recent Trends in Word Sense Disambiguation: A Survey
International Joint Conference on Artificial Intelligence, 2021 Word Sense Disambiguation (WSD) aims at making explicit the semantics of a word in context by identifying the most suitable meaning from a predefined sense inventory.
Michele Bevilacqua +3 more
semanticscholar +1 more source
Word Sense Disambiguation Using Clustered Sense Labels
Applied Sciences, 2022 Sequence labeling models for word sense disambiguation have proven highly effective when the sense vocabulary is compressed based on the thesaurus hierarchy.
Jeong Yeon Park +2 more
doaj +1 more source
Efficient estimation of Hindi WSD with distributed word representation in vector space
Journal of King Saud University: Computer and Information Sciences, 2022 Word Sense Disambiguation (WSD) is significant for improving the accuracy of the interpretation of a Natural language text. Various supervised learning-based models and knowledge-based models have been developed in the literature for WSD of the language ...
Archana Kumari, D.K. Lobiyal
doaj +1 more source
XL-WSD: An Extra-Large and Cross-Lingual Evaluation Framework for Word Sense Disambiguation
AAAI Conference on Artificial Intelligence, 2021 Transformer-based architectures brought a breeze of change to Word Sense Disambiguation (WSD), improving models' performances by a large margin. The fast development of new approaches has been further encouraged by a well-framed evaluation suite for ...
Tommaso Pasini +2 more
semanticscholar +1 more source
A Method of Word Sense Disambiguation with Recurrent Netural Networks
Journal of Harbin University of Science and Technology, 2020 Word sense disambiguation is an important research problem in natural language processing field. For the phenomenon that a Chinese word has many senses, recurrent neural network (RNN) is used to determine true meaning of ambiguous word with its context ...
ZHANG Chunxiang +2 more
doaj +1 more source
Determining the difficulty of Word Sense Disambiguation [PDF]
Journal of Biomedical Informatics, 2014 Automatic processing of biomedical documents is made difficult by the fact that many of the terms they contain are ambiguous. Word Sense Disambiguation (WSD) systems attempt to resolve these ambiguities and identify the correct meaning. However, the published literature on WSD systems for biomedical documents report considerable differences in ...
Mark Stevenson, Bridget T. McInnes
openaire +2 more sources
FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary [PDF]
Conference of the European Chapter of the Association for Computational Linguistics, 2021 Current models for Word Sense Disambiguation (WSD) struggle to disambiguate rare senses, despite reaching human performance on global WSD metrics. This stems from a lack of data for both modeling and evaluating rare senses in existing WSD datasets.
Terra Blevins +2 more
semanticscholar +1 more source
GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge [PDF]
Conference on Empirical Methods in Natural Language Processing, 2019 Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context. Traditional supervised methods rarely take into consideration the lexical resources like WordNet, which are widely utilized in knowledge-based ...
Luyao Huang +3 more
semanticscholar +1 more source