Unsupervised Learning of Textual Entailment Relations

Speaker: Ido Dagan (Bar-Ilan University)


We suggest that recognizing semantic entailment between texts may become a generic NLP task, which generalizes somewhat scattered efforts to cope with semantic variability in various application areas. Towards this goal, we present the result of ongoing research on unsupervised learning of basic entailment relations from corpora and the Web. Two different approaches will be presented, for learning the lexical entailment relationship between words, such as "company-firm" and "company-bank", and between more complex lexical-syntactic templates, such as "X prevent Y - X lowers the risk of Y". We will focus on understanding the different types of evidence that indicate the entailment relationship, including particular configurations of distributional features as well as fact-specific feature combinations, which we term anchor sets. Time permitting we may discuss aspects of the definition of lexical entailment.

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