Results 51 to 60 of about 84,634 (222)
Improved Word Sense Disambiguation with Enhanced Sense Representations
Conference on Empirical Methods in Natural Language Processing, 2021 Current state-of-the-art supervised word sense disambiguation (WSD) systems (such as GlossBERT and bi-encoder model) yield sur-prisingly good results by purely leveraging pre-trained language models and short dictionary definitions (or glosses) of the ...
Yang Song, Xin Cai Ong, H. Ng, Qian Lin
semanticscholar +1 more source
Boosting Applied to Word Sense Disambiguation [PDF]
, 2000 In this paper Schapire and Singer's AdaBoost.MH boosting algorithm is applied to the Word Sense Disambiguation (WSD) problem. Initial experiments on a set of 15 selected polysemous words show that the boosting approach surpasses Naive Bayes and Exemplar ...
Escudero, Gerard +2 more
core +6 more sources
Annual Meeting of the Association for Computational Linguistics, 2021 Lately proposed Word Sense Disambiguation (WSD) systems have approached the estimated upper bound of the task on standard evaluation benchmarks. However, these systems typically implement the disambiguation of words in a document almost independently ...
Ming Wang, Yinglin Wang
semanticscholar +1 more source
Word sense disambiguation and information retrieval [PDF]
, 1994 It has often been thought that word sense ambiguity is a cause of poor performance in Information Retrieval (IR) systems. The belief is that if ambiguous words can be correctly disambiguated, IR performance will increase.
Sanderson, M.
core +1 more source
Interpretability in Word Sense Disambiguation using Tsetlin Machine
International Conference on Agents and Artificial Intelligence, 2021 : Word Sense Disambiguation (WSD) is a longstanding unresolved task in Natural Language Processing. The challenge lies in the fact that words with the same spelling can have completely different senses, sometimes depending on subtle characteristics of ...
Rohan Kumar Yadav +3 more
semanticscholar +1 more source
Adapting BERT for Word Sense Disambiguation with Gloss Selection Objective and Example Sentences [PDF]
Findings, 2020 Domain adaptation or transfer learning using pre-trained language models such as BERT has proven to be an effective approach for many natural language processing tasks.
Boon Peng Yap +2 more
semanticscholar +1 more source
Word vs. Class-Based Word Sense Disambiguation [PDF]
, 2015 As empirically demonstrated by the Word Sense Disambiguation (WSD) tasks of the last SensEval/SemEval exercises, assigning the appropriate meaning to words in context has resisted all attempts to be successfully addressed.
Izquierdo Beviá, Rubén +2 more
core +2 more sources
Attention Neural Network for Biomedical Word Sense Disambiguation
Discrete Dynamics in Nature and Society, 2022 In order to improve the disambiguation accuracy of biomedical words, this paper proposes a disambiguation method based on the attention neural network. The biomedical word is viewed as the center. Morphology, part of speech, and semantic information from
Chun-Xiang Zhang +4 more
doaj +1 more source
WORD SENSE DISAMBIGUATION FOR TAMIL LANGUAGE USING PART-OF-SPEECH AND CLUSTERING TECHNIQUE [PDF]
Journal of Engineering Science and Technology, 2017 Word sense disambiguation is an important task in Natural Language Processing (NLP), and this paper concentrates on the problem of target word selection in machine translation.
P. ISWARYA, V. RADHA
doaj
A Corpus-Based Word Sense Disambiguation For Geez Language
Ethiopian Journal of Science and Sustainable Development, 2021 In natural language processing, languages have a number of ambiguous words and solving such kind of problem for the language can help the development of word sense disambiguation using corpusbased Approach.
Amlakie Aschale Alemu, Kinde Anlay Fante
doaj +1 more source