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	Recognizing textual entailment using probabilistic inference
	Sha, Lei ; Li, Sujian ; Jiang, Tingsong ; Chang, Baobao ; Sui, Zhifang
	2015
英文摘要	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. ? 2015 Association for Computational Linguistics.; EI; 1620-1625
语种	英语
出处	Conference on Empirical Methods in Natural Language Processing, EMNLP 2015
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/436906]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Sha, Lei,Li, Sujian,Jiang, Tingsong,et al. Recognizing textual entailment using probabilistic inference. 2015-01-01.

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