Abstract
Recognizing Textual Entailment is a task that recognizes pairs of natural language expressions. It is useful in a wide range of applications in NLP such as text summarization, question answering, and machine translation. In this thesis we focus on textual entailment recognition in Chinese. We first conduct an extensive survey of the vast literature on textual entailment recognition and related events, resources, tools, and common-used approaches. Besides, our experience of constructing a baseline Chinese textual entailment recognition system and the evaluation results are demonstrated. Based on the observation of the evaluation results, a sense decomposition-based word similarity measurement and a novel approach of detecting contradictions in text are proposed to tackle the issues of word similarity measurement and contradiction detection observed in baseline evaluations. The following evaluations show that our new integrated textual entailment recognition system outperforms the baseline system in almost every aspects in the dataset of NTCIR-10 RITE. Finally, the potential research directions of Chinese textual entailment recognition are listed in the end of the thesis.