Results 51 to 60 of about 83,914 (327)
DiBiMT: A Novel Benchmark for Measuring Word Sense Disambiguation Biases in Machine Translation
Annual 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
Conference 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
Word sense disambiguation in queries [PDF]
Proceedings of the 14th ACM international conference on Information and knowledge management, 2005 This paper presents a new approach to determine the senses of words in queries by using WordNet. In our approach, noun phrases in a query are determined first. For each word in the query, information associated with it, including its synonyms, hyponyms, hypernyms, definitions of its synonyms and hyponyms, and its domains, can be used for word sense ...
Shuang Liu, Clement Yu, Weiyi Meng
openaire +2 more sources
Unsupervised Word Sense Disambiguation Rivaling Supervised Methods
Annual 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
Fast max-margin clustering for unsupervised word sense disambiguation in biomedical texts. [PDF]
BMC Bioinformatics, 2009 Duan W, Song M, Yates A.
europepmc +3 more sources
Annual Meeting of the Association for Computational Linguistics, 2020 Neural architectures are the current state of the art in Word Sense Disambiguation (WSD). However, they make limited use of the vast amount of relational information encoded in Lexical Knowledge Bases (LKB).
Michele Bevilacqua, Roberto Navigli
semanticscholar +1 more source
Graph Convolutional Network for Word Sense Disambiguation
Discrete 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
Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation [PDF]
, 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
Conference on Empirical Methods in Natural Language Processing, 2020 Contextualized word embeddings have been employed effectively across several tasks in Natural Language Processing, as they have proved to carry useful semantic information. However, it is still hard to link them to structured sources of knowledge.
Bianca Scarlini +2 more
semanticscholar +1 more source
Hybrid Sense Classification Method for Large-Scale Word Sense Disambiguation
IEEE Access, 2020 Word sense disambiguation (WSD) is a task of determining a reasonable sense of a word in a particular context. Although recent studies have demonstrated some progress in the advancement of neural language models, the scope of research is still such that ...
Yoonseok Heo, Sangwoo Kang, Jungyun Seo
doaj +1 more source