CLC number: TP399:H03
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
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YANG Che-Yu. Word sense disambiguation using semantic relatedness measurement[J]. Journal of Zhejiang University Science A, 2006, 7(10): 1609-1625.
@article{title="Word sense disambiguation using semantic relatedness measurement",
author="YANG Che-Yu",
journal="Journal of Zhejiang University Science A",
volume="7",
number="10",
pages="1609-1625",
year="2006",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A1609"
}
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%A YANG Che-Yu
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%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A1609
TY - JOUR
T1 - Word sense disambiguation using semantic relatedness measurement
A1 - YANG Che-Yu
J0 - Journal of Zhejiang University Science A
VL - 7
IS - 10
SP - 1609
EP - 1625
%@ 1673-565X
Y1 - 2006
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2006.A1609
Abstract: All human languages have words that can mean different things in different contexts, such words with multiple meanings are potentially “ambiguous”. The process of “deciding which of several meanings of a term is intended in a given context” is known as “word sense disambiguation (WSD)”. This paper presents a method of WSD that assigns a target word the sense that is most related to the senses of its neighbor words. We explore the use of measures of relatedness between word senses based on a novel hybrid approach. First, we investigate how to “literally” and “regularly” express a “concept”. We apply set algebra to wordNet’s synsets cooperating with wordNet’s word ontology. In this way we establish regular rules for constructing various representations (lexical notations) of a concept using Boolean operators and word forms in various synset(s) defined in wordNet. Then we establish a formal mechanism for quantifying and estimating the semantic relatedness between concepts—we facilitate “concept distribution statistics” to determine the degree of semantic relatedness between two lexically expressed concepts. The experimental results showed good performance on Semcor, a subset of Brown corpus. We observe that measures of semantic relatedness are useful sources of information for WSD.
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