该文首先介绍一个逼近文本蕴涵关系的推理模型,它由带有推理规则集的蕴涵型式知识库和相关的概率评价构成。接着介绍习得推理规则和蕴涵型式及其概率的几种方法,包括从平行或单一语料库中学习和从网络文件中学习。然后介绍基于词汇概率的蕴涵识别模型,包括通过构建词汇蕴涵的概率模型和基于词汇所指的语义匹配模型来逼近文本蕴涵的几种方法。最后介绍基于句法的语义分析模型,包括基于依存树节点匹配、论元结构或原子命题匹配等处理模型。

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.
Key wordscomputer application; Chinese information processing; textual entailmentinference model; entailment pattern; recognizing models; lexical probability; syntax and semantics