scholarly journals Unsupervised word sense disambiguation rivaling supervised methods

1995 ◽  
Author(s):  
David Yarowsky
Keyword(s):  
Word Sense Disambiguation ◽  
Word Sense ◽  
Sense Disambiguation ◽  
Supervised Methods

scholarly journals A Learning-Based Approach for Biomedical Word Sense Disambiguation

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Hisham Al-Mubaid ◽  
Sandeep Gungu
Keyword(s):  
Research Effort ◽  
Word Sense Disambiguation ◽  
Interaction Model ◽  
Biomedical Domain ◽  
Word Sense ◽  
Text Annotation ◽  
Ambiguous Words ◽  
Sense Disambiguation ◽  
Supervised Methods ◽  
Automatic Text

In the biomedical domain, word sense ambiguity is a widely spread problem with bioinformatics research effort devoted to it being not commensurate and allowing for more development. This paper presents and evaluates a learning-based approach for sense disambiguation within the biomedical domain. The main limitation with supervised methods is the need for a corpus of manually disambiguated instances of the ambiguous words. However, the advances in automatic text annotation and tagging techniques with the help of the plethora of knowledge sources like ontologies and text literature in the biomedical domain will help lessen this limitation. The proposed method utilizes the interaction model (mutual information) between the context words and the senses of the target word to induce reliable learning models for sense disambiguation. The method has been evaluated with the benchmark dataset NLM-WSD with various settings and in biomedical entity species disambiguation. The evaluation results showed that the approach is very competitive and outperforms recently reported results of other published techniques.

scholarly journals WSD-GAN: Word Sense Disambiguation Using Generative Adversarial Networks

2019 ◽  
Vol 33 ◽  
pp. 9943-9944 ◽  
Author(s):  
Zijian Hu ◽  
Fuli Luo ◽  
Yutong Tan ◽  
Wenxin Zeng ◽  
Zhifang Sui
Keyword(s):  
Probability Distribution ◽  
Word Sense Disambiguation ◽  
Generative Adversarial Networks ◽  
Word Sense ◽  
Model Based ◽  
Knowledge Based ◽  
Sense Disambiguation ◽  
Supervised Methods ◽  
The Right ◽  
Word Senses

Word Sense Disambiguation (WSD), as a tough task in Natural Language Processing (NLP), aims to identify the correct sense of an ambiguous word in a given context. There are two mainstreams in WSD. Supervised methods mainly utilize labeled context to train a classifier which generates the right probability distribution of word senses. Meanwhile knowledge-based (unsupervised) methods which focus on glosses (word sense definitions) always calculate the similarity of context-gloss pair as score to find out the right word sense. In this paper, we propose a generative adversarial framework WSD-GAN which combines two mainstream methods in WSD. The generative model, based on supervised methods, tries to generate a probability distribution over the word senses. Meanwhile the discriminative model, based on knowledge-based methods, focuses on predicting the relevancy of the context-gloss pairs and identifies the correct pairs over the others. Furthermore, in order to optimize both two models, we leverage policy gradient to enhance the performances of the two models mutually. Our experimental results show that WSD-GAN achieves competitive results on several English all-words WSD datasets.

scholarly journals Multimodal Word Sense Disambiguation in Creative Practice

2020 ◽  
Author(s):  
Manuel Ladron de Guevara ◽  
Christopher George ◽  
Akshat Gupta ◽  
Daragh Byrne ◽  
Ramesh Krishnamurti
Keyword(s):  
Word Sense Disambiguation ◽  
Word Sense ◽  
Creative Practice ◽  
Sense Disambiguation

scholarly journals Spreading semantic information by Word Sense Disambiguation

2017 ◽  
Vol 132 ◽  
pp. 47-61 ◽  
Author(s):  
Yoan Gutiérrez ◽  
Sonia Vázquez ◽  
Andrés Montoyo
Keyword(s):  
Semantic Information ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Sense Disambiguation

Word Sense Disambiguation in the Biomedical Domain: An Overview

2005 ◽  
Vol 12 (5) ◽  
pp. 554-565 ◽  
Author(s):  
Martijn J. Schuemie ◽  
Jan A. Kors ◽  
Barend Mons
Keyword(s):  
Word Sense Disambiguation ◽  
Biomedical Domain ◽  
Word Sense ◽  
Sense Disambiguation
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