一种基于Opinosis图和马尔科夫随机游走模型的多文本情感摘要框架

doi:10.3969/j.issn.0372-2112.2017.12.024

电子学报 ›› 2017, Vol. 45 ›› Issue (12): 3005-3011.DOI: 10.3969/j.issn.0372-2112.2017.12.024

一种基于Opinosis图和马尔科夫随机游走模型的多文本情感摘要框架

康世泽, 马宏, 黄瑞阳

国家数字交换系统工程技术研究中心, 河南郑州 450002

收稿日期:2015-07-13 修回日期:2015-12-23 出版日期:2017-12-25
- 作者简介:
- 康世泽,男,1991年5月出生,内蒙古呼伦贝尔人.目前在国家数字交换系统工程技术研究中心攻读硕士学位,主要研究方向为数据挖掘、自然语言处理.E-mail:xdkangshize@163.com;马宏,男,1968年出生,江苏东台人.国家数字交换系统工程技术研究中心研究员,主要研究方向为数据挖掘、电信网信息关防;黄瑞阳,男,1986年出生,福建漳州人,博士.国家数字交换系统工程技术研究中心助理研究员,主要研究方向为文本挖掘、图挖掘.
- 基金资助:
- 国家自然基金 (No.61171108）; 国家重点基础研究发展计划 ("973"计划）基金 (No.2012CB315901，No.2012CB315905）

An Opinosis and MRW Based Sentiment Summarization Framework

KANG Shi-ze, MA Hong, HUANG Rui-yang

National Digital Switching System Engineering & Technological R & D Center, Zhengzhou, Henan 450002, China

Received:2015-07-13 Revised:2015-12-23 Online:2017-12-25 Published:2017-12-25

摘要/Abstract

摘要： 针对在线文本情感摘要生成问题，本文提出了一种基于Opinosis图和马尔科夫随机游走模型的情感摘要框架.首先，该框架将原始文本转化为Opinosis图，并利用其挖掘出文本中的特征词，这些特征词可以用来对原始文本的句子进行分类；其次本文在基于聚类的条件马尔科夫随机游走模型的基础上增加了情感层，改进后的模型可以判断同一聚类中各句子的情感倾向是否具有代表性并结合情感和聚类信息对句子进行排序.实验结果表明，本文提出的方法与基准算法相比在ROUGE（Recall-Oriented Understudy for Gisting Evaluation）值上具有明显提高.

关键词: Opinosis图, 马尔科夫随机游走模型, 情感摘要

Abstract: In order to produce summaries of online comment text,this paper presents a novel sentiment summarization framework which can produce abstractive summary based on Opinosis graph and Markov random walk model.This framework first convert the original text into Opinosis graph and use the Opinosis graph to mine the features of the original text,which can be used to classify the sentences.And then this paper adds a sentiment layer upon the cluster-based conditional Markov random walk nodel,and this improved model can judge which sentiment polar of the sentences in the same cluster is representative and select the proper sentence to produce abstractive summary based on the factors of sentiment and cluster.Experimental results show that this framework has achieved better results in ROUGE(Recall-Oriented Understudy for Gisting Evaluation)value compared to the baseline algorithm.

Key words: Opinosis graph, Markov random walk model, sentiment summarization

中图分类号:

TP391

康世泽, 马宏, 黄瑞阳. 一种基于Opinosis图和马尔科夫随机游走模型的多文本情感摘要框架[J]. 电子学报, 2017, 45(12): 3005-3011.

KANG Shi-ze, MA Hong, HUANG Rui-yang. An Opinosis and MRW Based Sentiment Summarization Framework[J]. Acta Electronica Sinica, 2017, 45(12): 3005-3011.

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一种基于Opinosis图和马尔科夫随机游走模型的多文本情感摘要框架

An Opinosis and MRW Based Sentiment Summarization Framework

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