Results 61 to 70 of about 83,914 (327)
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
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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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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
Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation [PDF]
Annual Meeting of the Association for Computational Linguistics, 2019 Contextual embeddings represent a new generation of semantic representations learned from Neural Language Modelling (NLM) that addresses the issue of meaning conflation hampering traditional word embeddings.
Daniel Loureiro, A. Jorge
semanticscholar +1 more source
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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Retrieving with good sense [PDF]
, 2000 Although always present in text, word sense ambiguity only recently became regarded as a problem to information retrieval which was potentially solvable.
Sanderson, M.
core +2 more sources
AMuSE-WSD: An All-in-one Multilingual System for Easy Word Sense Disambiguation
Conference on Empirical Methods in Natural Language Processing, 2021 Over the past few years, Word Sense Disambiguation (WSD) has received renewed interest: recently proposed systems have shown the remarkable effectiveness of deep learning techniques in this task, especially when aided by modern pretrained language models.
Riccardo Orlando +4 more
semanticscholar +1 more source
Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations [PDF]
Conference on Empirical Methods in Natural Language Processing, 2019 Contextualized word representations are able to give different representations for the same word in different contexts, and they have been shown to be effective in downstream natural language processing tasks, such as question answering, named entity ...
Christian Hadiwinoto +2 more
semanticscholar +1 more source
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