摘要
本文所针对的具体任务是抽取评价词和目标对象之间的关联关系。所采用的方法是将同一句子中共现的评价词与评价对象作为候选集合,应用最大熵模型并结合词、词性、语义和位置等特征进行关系抽取。我们将关系抽取引入观点挖掘,所提出的方法一定程度上解决了指代消解以及评价对象遗漏的问题。实验结果表明该方法的F值比取最近评价对象的Baseline方法有了15%的提高,并且发现程度副词能够帮助提高主观性关系抽取的性能。
This paper presents a novel method to extract the subjective relationship between opinion-bearing terms and opinion targets. This method extracted the pairs of opinion-bearing terms and opinion targets as the candidate set, and then employed the maximum entropy model to combine lexical, part of speech, semantic and positional features derived from text. Our method incorporates relation extraction into opinion mining and solves the problem of coreference and omitting of opinion targets to some extent. The experiments showed that the F value of our method is 15% higher than that of Baseline which takes the nearest opinion target as the real target, Besides, the experiments found that the intensifiers can improve the performance of subjective relation extraction.
出处
《中文信息学报》
CSCD
北大核心
2008年第2期55-59,86,共6页
Journal of Chinese Information Processing
基金
国家自然科学基金资助项目(60503070
60673038)
关键词
计算机应用
中文信息处理
观点挖掘
关系抽取
最大熵
computer application
Chinese information processing
opining mining
relation extraction
maximumentropy
作者简介
章剑锋（1982-），男，硕士生，研究方向为自然语言处理；
张奇（1981-），男，博士生，研究方向为自然语言处理；
吴立德（1937-），男，教授，博导，研究方向为计算机软件和应用。
