Word Sense Disambiguation Model with a Cache-Like Memory Module

TP391.1; Word sense disambiguation(WSD),identifying the specific sense of the target word given its context,is a fundamental task in natural language processing.Recently,researchers have shown promising results using long short term memory(LSTM),which is able to better capture sequential and syntact...

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Bibliographic Details
Published in东华大学学报（英文版） Vol. 38; no. 4; pp. 333 - 340
Main Authors LIN Qian, LIU Xin, XIN Chunlei, ZHANG Haiying, ZENG Hualin, ZHANG Tonghui, SU Jinsong
Format Journal Article
LanguageEnglish
Published School of Informatics,Xiamen University,Xiamen 361005,China 31.08.2021
Subjects
semantic dependencies
word sense disambiguation(WSD)
memory module
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Summary:TP391.1; Word sense disambiguation(WSD),identifying the specific sense of the target word given its context,is a fundamental task in natural language processing.Recently,researchers have shown promising results using long short term memory(LSTM),which is able to better capture sequential and syntactic features of text.However,this method neglects the dependencies among instances,such as their context semantic similarities.To solve this problem,we proposed a novel WSD model by introducing a cache-like memory module to capture the semantic dependencies among instances for WSD.Extensive evaluations on standard datasets demonstrate the superiority of the proposed model over various baselines.
ISSN:1672-5220
DOI:10.19884/j.1672-5220.202012114