ISSN 1000-1239 CN 11-1777/TP

• 人工智能 •

### 面向短文本分析的分布式表示模型

1. (山西大学计算机与信息技术学院 太原 030006) (计算智能与中文信息处理教育部重点实验室(山西大学) 太原 030006) (ljy@sxu.edu.cn)
• 出版日期: 2018-08-01
• 基金资助:
国家自然科学基金项目(U1435212,61432011,61573229)；山西省重点科技攻关项目(MQ2014-09) This work was supported by the National Natural Science Foundation of China (U1435212, 61432011, 61573229) and the Key Scientific and Technological Project of Shanxi Province (MQ2014-09).

### A Distributed Representation Model for Short Text Analysis

Liang Jiye, Qiao Jie, Cao Fuyuan,Liu Xiaolin

1. (School of Computer and Information Technology, Shanxi University, Taiyuan 030006) (Key Laboratory of Computational Intelligence and Chinese Information Processing (Shanxi University), Ministry of Education, Taiyuan 030006)
• Online: 2018-08-01

Abstract: The distributed representation of short texts has become an important task in text mining. However, the direct application of the traditional Paragraph Vector may not be suitable, and the fundamental reason is that it does not make use of the information of corpus in training process, so it can not effectively improve the situation of insufficient contextual information in short texts. In view of this, in this paper we propose a novel distributed representation model for short texts called BTPV (biterm topic paragraph vector). BTPV adds the topic information of BTM (biterm topic model) to the Paragraph Vector model. This method not only uses the global information of corpus, but also perfects the implicit vector of Paragraph Vector with the explicit topic information of BTM. At last, we crawl popular news comments from the Internet as experimental data sets, using K-Means clustering algorithm to compare the models’ representation performance. Experimental results have shown that the BTPV model can get better clustering results compared with the common distributed representation models such as word2vec and Paragraph Vector, which indicates the advantage of the proposed model for short text analysis.