关键词: 短文本聚类, LDA模型, Doc2vec模型, 学术摘要

Abstract:

Short text clustering for specific topics has become an important task in text data mining. The academic abstract text has poor stability of clustering results and semantic gap due to sparse data. Narrow domain leads to a large number of inconsequential word overlaps and making it hard to distinguish between topics and fine-grained clusters. In view of this, this paper proposes a novel clustering model called Topic Paragraph Vector model（Doc2vec-LDA, Doc-LDA）. By merging LDA topic model（Latent Dirichlet Allocation）and the Paragraph vector model（Doc2vec）, the model not only makes use of the information of the entire corpus in the model training process, but also uses the local semantic space information of Paragraph Vector to improve the implicit semantic information of LDA. Crawling academic abstracts from CNKI as experimental data sets, [K]-Means clustering algorithm is used to compare the abstract texts of each model. The experimental results show that the clustering effect based on Doc-LDA model is better than LDA, Word2vec and LDA+Word2vec models.

Key words: short text clustering, Latent Dirichlet Allocation（LDA） model, Doc2vec model, academic abstract