Recognizing Textual Entailment Using Probabilistic Inference

Abstract

Recognizing Text Entailment (RTE) plays an important role in NLP applications including question answering, information retrieval, etc.In recent work, some research explore "deep" expressions such as discourse commitments or strict logic for representing the text.However, these expressions suffer from the limitation of inference inconvenience or translation loss.To overcome the limitations, in this paper, we propose to use the predicate-argument structures to represent the discourse commitments extracted from text.At the same time, with the help of the YAGO knowledge, we borrow the distant supervision technique to mine the implicit facts from the text.We also construct a probabilistic network for all the facts and conduct inference to judge the confidence of each fact for RTE.The experimental results show that our proposed method achieves a competitive result compared to the previous work.