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Article type: Research Article
Authors: Yu, Jianpinga; b; * | Fu, Jilina; b; * | Bai, Tanac; b | Zhang, Taod; b | Li, Shaoxionge
Affiliations: [a] School of Foreign Studies, Yanshan University, Qinhuangdao, Hebei, P.R. of China | [b] Institute of Computational Linguistics, Yanshan University, Qinhuangdao, Hebei, P.R. of China | [c] Liren College, Yanshan University, Qinhuangdao, Hebei, P.R. of China | [d] Institute of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, P.R. of China | [e] School of Acupuncture-moxisition and Tuina, Shanghai University of Traditional Chinese Medicine, Shanghai, P.R. of China
Correspondence: [*] Corresponding authors. Jianping Yu, E-mail: yjp@ysu.edu.cn; Jilin Fu, E-mail: jlfu@ysu.edu.cn.
Abstract: Semantic merger, which is a phenomenon of semantic convergence of two meanings of a word in a certain context, is a kind of semantic indeterminacy in natural language, however, it brings trouble for natural language processing. Discovery of the features causing semantic mergers has been a significant but tough issue in natural language processing. Until now this issue has remained untouched. Therefore, in this article, this issue is studied. Based on a 1.8 million word English multi-genre corpus, taking English modal verb may as the target word, the contextual features causing semantic mergers between may(root possibility) and may(epistemic possibility) are investigated by an approach of attribute partial order diagram (APOD). First, the objects of may is categorized into 3 classes based on the idea of the three-way decision: may1(root possibility), may2(epistemic possibility) and may3(merger), then the rules for word sense disambiguation (WSD) of the three classes are extracted, respectively, and a comparison is made among the rules for different classes, and finally the features causing semantic merger of may are discovered. The discovered knowledge provides valuable evidence for finding the semantic merger, the cause of the semantic merger and the solution of semantic mergers of may, and the proposed approach can also be use for other modal verbs, which may benefit the natural language processing of English modal verbs.
Keywords: Semantic merger, features causing semantic merger, English modal verb, attribute partial order diagram
DOI: 10.3233/JIFS-220388
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6383-6393, 2022
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