摘要
该文分析了现有基于分类策略的文本蕴涵识别方法的问题,并提出了一种基于知识话题模型的文本蕴涵分类识别方法。其假设是:文本可看作是语义关系的组合,这些语义关系构成若干话题;若即若文本T蕴涵假设H,说明T和H具有相似的话题分布,反之说明T和H不具有相似的话题分布。基于此,我们将T和H的蕴涵识别问题转化为相关话题的生成过程,同时将文本推理知识融入到抽样过程,由此建立一个面向文本蕴涵识别的话题模型。实验结果表明基于知识话题模型在一定程度上改进了文本蕴涵识别系统的性能。
This paper analyzes the defects in current entailment recognition approaches based on classification strate gy and proposes a novel approach to recognizing textual entailment based on a knowledge topic model. The assumption in this approach is, if two texts have an entailment relation, they should share a same or similar topic distribu tion. The approach builds an LDA model to estimate semantic similarities between each text and hypothesis, which provides the evidences for judging entailment relation. We also employ three knowledge bases to improve the precision of Gibbs sampling. Experiments show that knowledge topic model improves the performance of textual entailment recognition systems.
出处
《中文信息学报》
CSCD
北大核心
2015年第6期119-126,共8页
Journal of Chinese Information Processing
基金
国家自然科学基金(61402341
61173062
61373108)
国家社会科学基金重大项目(11&ZD189)
中国博士后科学基金(2013M540594)
关键词
文本蕴涵识别
话题模型
蕴涵分类
推理知识
recognizing textual entailment
topic models entailment classification
inference knowledge
