计算机工程2017，Vol.43Issue(4)：222-227,6.DOI:10.3969/j.issn.1000-3428.2017.04.038
基于频繁子树模式的评价对象抽取
Extraction of Opinion Targets Based on Frequent Sub-tree Pattern
摘要
Abstract
Most existing opinion target extraction methods are based on the heuristic rules or machine learning using features such as part of speech,morphology and etc.,but the defect of these methods is that deep association relationship mined by dependency syntax analysis is not used.In order to solve this problem,a novel opinion target extraction method for Chinese short critical texts is proposed based on frequent tree patterns mined from dependency relation tree bank.First,this method labels the initial tagging opinion target based on frequent sub-tree patterns,and then it trains out an ordered rule set based on error-driven TBL framework which can be related to the combination of opinion targets.Finally,opinion target is extracted based on the ordered rule set.Experimental results show that this method has good stability and precision,and is better than Support Vector Machine(SVM)-based method on indicators such as recall and F1-score.关键词
依存句法/短文本/频繁子树模式/错误驱动/支持向量机Key words
dependency syntax/short text/frequent sub-tree pattern/error driven/Support Vector Machine(SVM)分类
信息技术与安全科学引用本文复制引用
田卫东,苗惠君..基于频繁子树模式的评价对象抽取[J].计算机工程,2017,43(4):222-227,6.基金项目
国家"863"计划项目(2012AA011005) （2012AA011005）
国家自然科学基金(61273292) （61273292）
情感计算与先进智能机器安徽省重点实验室开放课题(ACAIM2015xxx). （ACAIM2015xxx）
