Results 41 to 50 of about 82,228 (264)
International 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]
, 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
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Word sense disambiguation with pictures [PDF]
Proceedings of the HLT-NAACL 2003 workshop on Learning word meaning from non-linguistic data -, 2003 We introduce a method for using images for word sense disambiguation, either alone, or in conjunction with traditional text based methods. The approach is based in recent work on a method for predicting words for images which can be learned from image datasets with associated text.
David Forsyth +2 more
openaire +2 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
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
Improving Japanese Zero Pronoun Resolution by Global Word Sense Disambiguation [PDF]
, 2004 This paper proposes unsupervised word sense disambiguation based on automatically constructed case frames and its incorporation into our zero pronoun resolution system. The word sense disambiguation is applied to verbs and nouns.
Daisuke Kawahara, Sadao Kurohashi
core +1 more source
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
Word sense disambiguation with pictures
Artificial Intelligence, 2005 AbstractWe introduce using images for word sense disambiguation, either alone, or in conjunction with traditional text based methods. The approach is based on a recently developed method for automatically annotating images by using a statistical model for the joint probability for image regions and words. The model itself is learned from a data base of
JohnsonMatthew, BarnardKobus
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A Method of Word Sense Disambiguation with Restricted Boltzmann Machine
Journal of Harbin University of Science and Technology, 2019 For polysemy phenomenon in Chinese, Restricted Boltzmann Machine (RBM) is adopted to determine the true meaning of ambiguous vocabulary where linguistic knowledge in context is used Word form, part of speech and semantic categories in four left and ...
ZHANG Chun-xiang +2 more
doaj +1 more source
Complexity, 2020 In natural language, the phenomenon of polysemy is widespread, which makes it very difficult for machines to process natural language. Word sense disambiguation is a key issue in the field of natural language processing.
Lei Wang, Qun Ai
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