Word Sense Disambiguation Model with a Cache-Like Memory Module

• 作者: LIN Qian LIU Xin XIN Chunlei ZHANG Haiying ZENG Hualin ZHANG Tonghui SU Jinsong
• 来源期刊: 东华大学学报（英文版）
• 年，卷（期）: 2021,（4）
• 所属期刊栏目: Artificial Intelligence and Communication Technology
• 页码范围: 333-340
• 页数: 8页
• 分类号: TP391.1
• 语种: 英文
• DOI: 10.19884/j.1672-5220.202012114

摘要

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.