Open Access

ARTICLE

Improving Chinese Word Representation with Conceptual Semantics

Tingxin Wei^{1, 2}, Weiguang Qu^{2, 3, *}, Junsheng Zhou³, Yunfei Long⁴, Yanhui Gu³, Zhentao Xia³

1 International College for Chinese Studies, Nanjing Normal University, Nanjing, 210097, China.
2 School of Chinese Language and Literature, Nanjing Normal University, Nanjing, 210097, China.
3 School of Computer Science and Technology, Nanjing Normal University, Nanjing, 210023, China.
4 School of Computer Science and Electronic Engineering, University of Essex, Essex, CO4 3DQ, UK.

* Corresponding Author: Weiguang Qu. Email: .

Computers, Materials & Continua 2020, 64(3), 1897-1913. https://doi.org/10.32604/cmc.2020.010813

Received 31 March 2020; Accepted 08 May 2020; Issue published 30 June 2020

Abstract

The meaning of a word includes a conceptual meaning and a distributive meaning. Word embedding based on distribution suffers from insufficient conceptual semantic representation caused by data sparsity, especially for low-frequency words. In knowledge bases, manually annotated semantic knowledge is stable and the essential attributes of words are accurately denoted. In this paper, we propose a Conceptual Semantics Enhanced Word Representation (CEWR) model, computing the synset embedding and hypernym embedding of Chinese words based on the Tongyici Cilin thesaurus, and aggregating it with distributed word representation to have both distributed information and the conceptual meaning encoded in the representation of words. We evaluate the CEWR model on two tasks: word similarity computation and short text classification. The Spearman correlation between model results and human judgement are improved to 64.71%, 81.84%, and 85.16% on Wordsim297, MC30, and RG65, respectively. Moreover, CEWR improves the F1 score by 3% in the short text classification task. The experimental results show that CEWR can represent words in a more informative approach than distributed word embedding. This proves that conceptual semantics, especially hypernymous information, is a good complement to distributed word representation.

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Keywords

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