Chinese textual entailment recognition based on syntactic tree clipping

Abstract

Textual entailment has been proposed as a unifying generic framework for modeling language variability and semantic inference in different Natural Language Processing (NLP) tasks. This paper presents a novel statistical method for recognizing Chinese textual entailment in which lexical, syntactic with semantic matching features are combined together. In order to solve the problems of syntactic tree matching difficulty and tree structure errors caused by Chinese word segmentation, the method firstly clips the syntactic trees into minimum information trees and then computes syntactic matching similarity on them. All features will be used in a voting style under different machine learning methods to predict whether the text sentence can entail the hypothesis sentence in a text-hypothesis pair. The experimental results show that the feature on changing structure of syntactic tree is effective and efficient in Chinese textual entailment.