Results 31 to 40 of about 58,551 (288)
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
Word domain disambiguation via word sense disambiguation [PDF]
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers on XX - NAACL '06, 2006 Word subject domains have been widely used to improve the performance of word sense disambiguation algorithms. However, comparatively little effort has been devoted so far to the disambiguation of word subject domains. The few existing approaches have focused on the development of algorithms specific to word domain disambiguation.
Sanfilippo, Antonio P. +2 more
openaire +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
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
Analysis and Evaluation of Language Models for Word Sense Disambiguation
International Conference on Computational Logic, 2021 Transformer-based language models have taken many fields in NLP by storm. BERT and its derivatives dominate most of the existing evaluation benchmarks, including those for Word Sense Disambiguation (WSD), thanks to their ability in capturing context ...
Daniel Loureiro +3 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
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
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
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
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