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 strategy 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 distribution. 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.
Key words recognizing textual entailment; topic model; entailment classification;inference knowledge