Chinese Evaluation Element Extraction Based on Cascaded Model

WANG Ya-shen; HUANG He-yan; FENG-Chong; LIU Quan-chao

doi:10.3969/j.issn.0372-2112.2016.10.025

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Chinese Evaluation Element Extraction Based on Cascaded Model

更新时间：2025-07-16

- Chinese Evaluation Element Extraction Based on Cascaded Model
- Acta Electronica Sinica Vol. 44, Issue 10, Pages: 2459-2465(2016)
- 作者机构：
  
  北京理工大学计算机学院北京市海量语言信息处理与云计算应用工程技术研究中心,北京,100081
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2016.10.025
  CLC： TP391.1
- Published：2016
- 稿件说明：
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WANG Ya-shen, HUANG He-yan, FENG-Chong, et al. Chinese Evaluation Element Extraction Based on Cascaded Model[J]. Acta Electronica Sinica, 2016, 44(10): 2459-2465.
DOI：

WANG Ya-shen, HUANG He-yan, FENG-Chong, et al. Chinese Evaluation Element Extraction Based on Cascaded Model[J]. Acta Electronica Sinica, 2016, 44(10): 2459-2465. DOI： 10.3969/j.issn.0372-2112.2016.10.025.

摘要

随着社交媒体的发展及成熟，每天在互联网环境中都会产生大量的用户评论信息.抽取评价短语、评价对象和观点持有者等情感要素，已经成为了中文观点挖掘和情感分析的重要先决任务.针对中文情感要素抽取任务，本文提出了一个统计和规则相结合的级联模型，主要贡献包括：（1）针对汽车领域评论信息，构建情感要素标注语料库和相关词典；（2）对于以往研究较少关注的中文评价短语，本文详细分析阐述其定义和分类；（3）结合统计和规则，分别针对评价短语和情感要素提出级联抽取策略.实验结果充分证明了该级联模型的有效性，相比较于其它基于规则的情感要素抽取算法有效提升了召回率，同时为后续社交媒体情感分析任务提供了有力的支持.

Abstract

With the development of social media

massive reviews are generated by users every day.The extraction of evaluation elements

including evaluation phrase

comment target and opinion holder

is an important pre-task of Chinese opinion mining and sentiment analysis.This paper proposes an efficient method for extracting Chinese evaluation elements based on cascaded model and mainly makes three contributions:(i) to implement and evaluate the method

we construct an original annotated corpus for Chinese evaluation elements of automobile;(ii) we provide specific definition and classification of Chines evaluation phrase;(iii) combing statistic method and rule-based method

we present cascaded strategy for extraction of evaluation phrase and evaluation elements

respectively.According to the experiment results

the proposed method performs well

and effectively improve the recall compared with other rule-based algorithm.Meanwhile it contributes greatly to our subsequent tasks

such as sentiment analysis of social media.

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