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山东大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (3): 67-74.doi: 10.6040/j.issn.1672-3961.0.2017.402

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一种长文本辅助短文本的文本理解方法

闫盈盈1,2,黄瑞章1,2*,王瑞1,2,马灿1,2,刘博伟1,2,黄庭1,2   

  1. 1. 贵州大学计算机科学与技术学院, 贵州 贵阳 550025;2. 贵州省公共大数据重点实验室, 贵州 贵阳 550025
  • 收稿日期:2017-08-23 出版日期:2018-06-20 发布日期:2017-08-23
  • 通讯作者: 黄瑞章(1979— ),女,天津人,副教授,博士,主要研究方向为数据挖掘与机器学习. E-mail:rzhuang@gzu.edu.cn E-mail:yyingy0921@163.com
  • 作者简介:闫盈盈(1991— ),女,山西吕梁人,硕士研究生,主要研究方向为数据挖掘与机器学习. E-mail:yyingy0921@163.com
  • 基金资助:
    国家自然科学基金资助项目(61462011,61540050);贵州大学引进人才科研资助项目(2011015);贵州省重大应用基础研究资助项目(JZ20142001)

A document understanding method for short texts by auxiliary long documents

YAN Yingying1,2, HUANG Ruizhang1,2*, WANG Rui1,2, MA Can1,2, LIU Bowei1,2, HUANG Ting1,2   

  1. 1. School of Computer Science and Technology, Guizhou University, Guiyang 550025, Guizhou, China;
    2. Guizhou Provincial Key Laboratory of Public Big Data, Guiyang 550025, Guizhou, China
  • Received:2017-08-23 Online:2018-06-20 Published:2017-08-23

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摘要： 在狄利克雷多项回归(dirichlet-multinomial regression, DMR)模型的基础上,提出一个长文本辅助短文本理解的二元狄利克雷多项回归(dual dirichlet-multinomial regression, DDMR)模型。来自不同数据源的长短文本共享一个主题集合,并采用不同的狄利克雷先验产生长短文本的主题分配,使得长文本的主题知识能够迁移到短文本中,改善短文本的理解。试验表明,DDMR模型在短文本的主题发现效果上具有较大的提升作用。

关键词: 短文本理解, 主题模型, 二元狄利克雷多项回归模型

Abstract: Based on the dirichlet-multinomial regression(DMR)model, a dual dirichlet-multinomial regression(DDMR)model that short texts were understood by auxiliary long documents was proposed. A topic set was shared by long documents and short texts which came from different data sources, and two dirichlet priors were used to generate the topic allocation of long documents and short texts, which enabled the topic knowledge of long documents to be transferred to short texts and improved understanding of the short text. The experiments showed that the DDMR model had a great effect on the topical discovery of short texts.

Key words: short text understanding, dual dirichlet-multinomial regression model, topic model

中图分类号: 

  • TP391.1
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