Recognizing Textual Entailment (RTE) is to detect an important relation between two texts, namely whether one text can be inferred from the other. For natural language processing, especially for natural language understanding, this is a useful and challenging task. We start with an introduction of the notion of textual entailment, and then define the scope of the recognition task. We summarize previous work and point out two important issues involved, meaning representation and relation recognition. For the former, a general representation based on dependency relations between words or tokens is used to approximate the meaning of the text. For the latter, two categories of approaches, intrinsic and extrinsic ones, are proposed. The two parts of the thesis are dedicated to these two classes of approaches. Intrinsically, we develop specialized modules to deal with different types of entailment; and extrinsically, we explore the connection between RTE and other semantic relations between texts.

A two-way Textual Entailment (TE) recognition system that uses lexical and syntactic features has been described in this paper. The TE system is rule based that uses lexical and syntactic similarities. The important lexical similarity features that are used in the present system are: WordNet based uni-gram match, bi-gram match, longest common sub-sequence, skip-gram, stemming. In the syntactic TE system, the important features used are: subject-subject comparison, subject-verb comparison, object-verb comparison and cross subject-verb comparison. The system has been separately trained on each development corpus released as part of the Recognising Textual Entailment (RTE) competitions RTE-1, RTE-2, RTE-3 and RTE-5 and tested on the respective RTE test sets. No separate development data was released in RTE-4. The evaluation results on each test set are compared with the RTE systems that participated in the respective RTE competitions with lexical and syntactic approaches.