Determine Word Sense Based on Semantic and Syntax Information

Zhang, Chunxiang; Sun, Li; Gao, Xue-Yao

doi:10.14257/ijdta.2016.9.2.03

IJDTA

2016

DOI: 10.14257/ijdta.2016.9.2.03

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Determine Word Sense Based on Semantic and Syntax Information

Chunxiang Zhang¹,

Li Sun²,

Xue-Yao Gao³

Abstract: Abstract

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2021

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Word sense disambiguation based on stretchable matching of the semantic template

Wang¹,

Huang²,

Yu³

2021

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It is evident that the traditional hard matching of a fixed-length template cannot satisfy the nearly indefinite variations in natural language. This issue mainly results from three major problems of the traditional matching mode: 1) in matching with a short template, the context of natural language cannot be effectively captured; 2) in matching with a long template, serious data sparsity will lead to a low success rate of template matching (i.e., low recall); and 3) due to a lack of flexible matching ability, traditional hard matching is more prone to failure. Therefore, this paper proposed a novel method of stretchable matching of the semantic template (SMOST) to deal with the above problems. We have applied this method to word sense disambiguation in the natural language processing field. In the same case of using only the SemCor corpus, the result of our system is very close to the best result of existing systems , which shows the effectiveness of new proposed method.

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Word sense disambiguation based on stretchable matching of the semantic template

Wang¹,

Huang²,

Yu³

2021

MFC

View full text Add to dashboard Cite

show abstract

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Determine Word Sense Based on Semantic and Syntax Information

Abstract: Abstract

Cited by 1 publication

References 12 publications

Word sense disambiguation based on stretchable matching of the semantic template

Word sense disambiguation based on stretchable matching of the semantic template

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