Research on short text classification based on keyword similarity

Zhang Zhenhao1
Guo Yi1,2,3
Han Meiqi1
Wang Jixiang1
1. School of Information Science & Engineering, East China University of Science & Technology, Shanghai 200237, China
2. School of Information Science & Technology, Shihezi University, Shihezi Xinjiang 832003, China
3. Business Intelligence & Visualization Research Center, National Engineering Laboratory for Big Data Distribution & Exchange Technologies, Shanghai 200436, China

Abstract

In order to cope with the problem of data sparsity and curse of dimensionality in text classification, this paper proposed a short text classification framework by taking keyword as features and assigning keyword similarity as feature weight. First, it trained a word2vec model with large corpus data, then got keywords of each category text by textrank. And it selected unique keywords from the keywords collection as features. For each feature, it calculated the similarity of words in the short text by word2vec model, and assigned the maximum similarity as the weight of the feature. Finally, it chose KNN and SVM as classifier. Experiments on dataset of Chinese news headlines demonstrate that the accuracy outperforms other usual methods by 6%.

Foundation Support

国家自然科学基金资助项目(61462073)
上海市科学技术委员会项目(17DZ1101003,18511106602)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.04.0440
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 1
Section: Algorithm Research & Explore
Pages: 26-29
Serial Number: 1001-3695(2020)01-005-0026-04

Publish History

[2020-01-05] Printed Article

Cite This Article

张振豪, 过弋, 韩美琪, 等. 基于关键词相似度的短文本分类方法研究 [J]. 计算机应用研究, 2020, 37 (1): 26-29. (Zhang Zhenhao, Guo Yi, Han Meiqi, et al. Research on short text classification based on keyword similarity [J]. Application Research of Computers, 2020, 37 (1): 26-29. )

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