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

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%.

Automated summary

This chapter proposes the use of a contextual Word2Vec model for understanding OOV. The authors extract the OOV using left-right entropy and point information entropy. They construct a word vector space using Word2Vec and obtain contextual information using CBOW. The results show that the proposed model achieves a higher accuracy rate than Skip-Gram.