This paper describes our system of recognizing textual entailment for RITEVAL System Validation and Fact Validation subtasks at NTCIR-11. For System Validation subtask, we employ a transformation model and acquire entailment rules by extracting synonyms and inferable expressions from resources such as lexicons and knowledge bases. Also, a cascaded entailment recognition model is employed to recognize four types of entailment relations. For Fact Validation subtask, we build a pipeline approach

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... find texts that entails given texts. First, a retrieval model is used to search related sentences from Wikipedia documents provided, then we used the recognition model in System Validation subtask to find such sentences that entailed the given texts. Official results show that our system achieves a performance of 53.48% MacroF1 score in Chinese SVBC subtask, a 25.74% MacroF1 score in Chinese SVMC subtask, a 45.51% MacroF1 score in English FV subtask and a 38.08% MacroF1 score in Chinese FV subtask.