基于依赖联系分析的观点词对协同抽取

doi:10.11896/jsjkx.190600153

计算机科学 ›› 2020, Vol. 47 ›› Issue (8): 164-170.doi: 10.11896/jsjkx.190600153

• 数据库&大数据&数据科学 • 上一篇下一篇

基于依赖联系分析的观点词对协同抽取

赵威^{1, 2}, 林煜明¹, 王超强¹, 蔡国永¹

1 桂林电子科技大学广西可信软件重点实验室广西桂林 541004
2 华东师范大学数据科学与工程学院上海 200062

出版日期:2020-08-15 发布日期:2020-08-10
通讯作者: 林煜明(ymlin@guet.edu.cn)
作者简介:331205121@qq.com
基金资助:
广西自然科学基金(2018GXNSFDA281049);国家自然科学基金(61662015, U1711263);广西创新驱动发展专项资金项目(桂科AA19046004);桂林电子科技大学研究生教育创新计划资助项目(2018YJCX48);广西可信软件重点实验室研究课题(kx201916)

Opinion Word-pairs Collaborative Extraction Based on Dependency Relation Analysis

ZHAO Wei^{1, 2}, LIN Yu-ming¹, WANG Chao-qiang¹, CAI Guo-yong¹

1 Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China
2 School of Data Science & Engineering, East China Normal University, Shanghai 200062, China

Online:2020-08-15 Published:2020-08-10
About author:ZHAO Wei, born in 1995, postgraduate.His main research interests include opinion mining and so on.
LIN Yu-ming, born in 1978, Ph.D, professor.His main research interests include opinion mining, knowledge graph, and massive data management.
Supported by:
This work was supported by the Guangxi Natural Science Foundation (2018GXNSFDA281049), National Natural Science Foundation of China (61662015, U1711263), Science and Technology Major Project of Guangxi Province (AA19046004) and Innovation Project of Guet Graduate Education (2018YJCX48) and Project of Guangxi Key Laboratory of Trusted Software(kx201916).

摘要/Abstract

摘要： 同一类商品下, 观点词对中包含的观点目标和观点词通常有着很强的观点依赖联系, 因此可以通过对评论句子中单词间的观点依赖联系进行分析来提取观点词对。首先, 构建评论句子的依赖联系分析模型来获取评论句子中每个单词之间的依赖联系信息, 文中选择的基本模型是LSTM神经网络;然后, 假设评论句子中所包含的观点词对中的一项是已知的, 并将该已知项作为模型的注意力信息, 使得模型能够从评论句子中有重点地提取出与该已知项具有强观点依赖联系的单词或词组, 并将其作为观点词对中的另一未知项;最后, 将观点依赖联系得分最高的词对作为观点词对并输出。文中进一步设计了一种复合模型, 通过结合两种包含不同已知项信息的上述模型, 来实现在不需要提前知道已知项的情况下观点词对的挖掘。

关键词: 观点词对, 观点依赖联系分析, 神经网络, 注意力机制

Abstract: In the same category of commodities, opinion word-pairs usually have strong opinion dependence relation to the opinion targets and the opinion words contained in them.Therefore, in the extraction process of opinion word-pairs, they can be extracted by analyzing the opinion dependence relations among the words in the review sentences.Firstly, a dependency relation analysis model is constructed to obtain the dependency relation information of each word in a review sentence, and the basic model is defined as LSTM neural network.Secondly, it is assumed that one of the item that opinion word-pairs contained in review sentence is known, and the known item is used as the model’s attention information, so that the model can focus on extracting the words of phrases associated with the known item with strong opinion dependence from the review sentence as another unknown item in the opinion word-pairs.Finally, the word-pairs with the highest score of the opinion dependence relation are output as the opinion word-pairs.Then a compound model is designed to realize the mining of opinion word pairs without knowing the known items in advance by combining the two models which contain the information of different known items in the opinion word-pairs.

Key words: Attention mechanism, Neural network, Opinion dependency relation analysis, Opinion pair

中图分类号:

TP391

赵威, 林煜明, 王超强, 蔡国永. 基于依赖联系分析的观点词对协同抽取[J]. 计算机科学, 2020, 47(8): 164-170. https://doi.org/10.11896/jsjkx.190600153

ZHAO Wei, LIN Yu-ming, WANG Chao-qiang, CAI Guo-yong. Opinion Word-pairs Collaborative Extraction Based on Dependency Relation Analysis[J]. Computer Science, 2020, 47(8): 164-170. https://doi.org/10.11896/jsjkx.190600153

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基于依赖联系分析的观点词对协同抽取

Opinion Word-pairs Collaborative Extraction Based on Dependency Relation Analysis

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