Results 31 to 40 of about 83,408 (318)
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
Research on Unsupervised Word Sense Disambiguation [PDF]
Journal of Software, 2009 Ruiqin Wang, Fansheng Kong
openalex +3 more sources
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
ConSeC: Word Sense Disambiguation as Continuous Sense Comprehension
Conference on Empirical Methods in Natural Language Processing, 2021 Supervised systems have nowadays become the standard recipe for Word Sense Disambiguation (WSD), with Transformer-based language models as their primary ingredient.
Edoardo Barba +2 more
semanticscholar +1 more source
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
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
SBU-WSD-Corpus: A Sense Annotated Corpus for Persian All-words Word Sense Disambiguation [PDF]
International Journal of Web Research, 2022 Word Sense Disambiguation (WSD) is a long standing task in Natural Language Processing (NLP) that aims to automatically identify the most relevant meaning of the words in a given context.
Hossein Rouhizadeh +2 more
doaj +1 more source
Transfer Learning and Augmentation for Word Sense Disambiguation [PDF]
European Conference on Information Retrieval, 2021 Many downstream NLP tasks have shown significant improvement through continual pre-training, transfer learning and multi-task learning. State-of-the-art approaches in Word Sense Disambiguation today benefit from some of these approaches in conjunction ...
Harsh Kohli
semanticscholar +1 more source
IET Communications, EarlyView., 2022 This paper proposed an Automated Quality Assessment of SRS (AQA‐SRS) framework by integrating four popular methods which are; NLP, K‐means, MAS, and CBR to assess the quality of SRS documents. The NLP utilize for feature extraction, K‐means for features clustering, MAS for interactive assessment and feature selection decision, and CBR for managing the ...
Mohammed Ahmed Jubair +6 more
wiley +1 more source