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Contextual Word2Vec Model for Understanding Chinese Out of Vocabularies on Online Social Media

JiaKai Gu, Li, Nam D. Vo, Jason J. Jung

Source Title: International Journal on Semantic Web and Information Systems (IJSWIS)18(1)

ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781799893967|DOI: 10.4018/IJSWIS.309428

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MLA

Gu, JiaKai, et al. "Contextual Word2Vec Model for Understanding Chinese Out of Vocabularies on Online Social Media." IJSWIS vol.18, no.1 2022: pp.1-14. http://doi.org/10.4018/IJSWIS.309428

APA

Gu, J., Li, Vo, N. D., & Jung, J. J. (2022). Contextual Word2Vec Model for Understanding Chinese Out of Vocabularies on Online Social Media. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-14. http://doi.org/10.4018/IJSWIS.309428

Chicago

Gu, JiaKai, et al. "Contextual Word2Vec Model for Understanding Chinese Out of Vocabularies on Online Social Media," International Journal on Semantic Web and Information Systems (IJSWIS) 18, no.1: 1-14. http://doi.org/10.4018/IJSWIS.309428

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Abstract

In this chapter, the authors propose to use contextual Word2Vec model for understanding OOV (out of vocabulary). The OOV is extracted by using left-right entropy and point information entropy. They choose to use Word2Vec to construct the word vector space and CBOW (continuous bag of words) to obtain the contextual information of the words. If there is a word that has similar contextual information to the OOV, the word can be used to understand the OOV. They chose the Weibo corpus as the dataset for the experiments. The results show that the proposed model achieves 97.10% accuracy, which is better than Skip-Gram by 8.53%.