The Inference and Identification Models for Textual Entailment
Minghua Wang · Zhongwen xinxi xuebao · 2010
This article firstly presents an inference model that consists of a knowledge base of entailment patterns along with a set of inference rules and related probability estimations,which approximates the textual entailment relationship and predicates whether an entailment holds for a given text-hypothesis pair.Then it introduces some methods of learning the inference rules and entailment patterns and their probability,including learning from a single or parallel/comparable corpus,or from the web.Finally,it describes the recognizing entailment models which based on lexical probability,e.g.lexical entailment probability models and lexical reference matching models,and the syntax and semantics driven models,e.g.the models based on the matching the dependency tree nodes or predicate-argument structures between a given text-hypothesis pair.