Results 31 to 40 of about 84,634 (222)

SBU-WSD-Corpus: A Sense Annotated Corpus for Persian All-words Word Sense Disambiguation [PDF]

open access: yesInternational 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

GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge [PDF]

open access: yesConference 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

Comparative Analysis of Recurrent Neural Network Architectures for Arabic Word Sense Disambiguation

open access: yesInternational Conference on Web Information Systems and Technologies, 2022
: Word Sense Disambiguation (WSD) refers to the process of discovering the correct sense of an ambiguous word occurring in a given context. In this paper, we address the problem of Word Sense Disambiguation of low-resource languages such as Arabic ...
R. Saidi, Fethi Jarray, M. Alsuhaibani
semanticscholar   +1 more source

Transfer Learning and Augmentation for Word Sense Disambiguation [PDF]

open access: yesEuropean 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

DiBiMT: A Novel Benchmark for Measuring Word Sense Disambiguation Biases in Machine Translation

open access: yesAnnual Meeting of the Association for Computational Linguistics, 2022
Lexical ambiguity poses one of the greatest challenges in the field of Machine Translation. Over the last few decades, multiple efforts have been undertaken to investigate incorrect translations caused by the polysemous nature of words.
Niccolò Campolungo   +3 more
semanticscholar   +1 more source

Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration

open access: yesConference of the European Chapter of the Association for Computational Linguistics, 2021
Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem in which one is asked to choose only the best-fitting sense for a target word, given its context.
Simone Conia, Roberto Navigli
semanticscholar   +1 more source

Unsupervised Word Sense Disambiguation Rivaling Supervised Methods

open access: yesAnnual Meeting of the Association for Computational Linguistics, 1995
This paper presents an unsupervised learning algorithm for sense disambiguation that, when trained on unannotated English text, rivals the performance of supervised techniques that require time-consuming hand annotations.
David Yarowsky
semanticscholar   +1 more source

Graph Convolutional Network for Word Sense Disambiguation

open access: yesDiscrete Dynamics in Nature and Society, 2021
Word sense disambiguation (WSD) is an important research topic in natural language processing, which is widely applied to text classification, machine translation, and information retrieval.
Chun-Xiang Zhang   +3 more
doaj   +1 more source

Abstractive Text Summarization: Enhancing Sequence-to-Sequence Models Using Word Sense Disambiguation and Semantic Content Generalization

open access: yesInternational Conference on Computational Logic, 2021
Nowadays, most research conducted in the field of abstractive text summarization focuses on neural-based models alone, without considering their combination with knowledge-based approaches that could further enhance their efficiency.
P. Kouris   +2 more
semanticscholar   +1 more source

Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation [PDF]

open access: yes, 2017
Interpretability of a predictive model is a powerful feature that gains the trust of users in the correctness of the predictions. In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpretable than knowledge-free ...
Biemann, Chris   +6 more
core   +3 more sources

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