基于认知语言学的自然语言语义表示方法

Natural Language Semantic Representation Based on Cognitive Linguistics

语义网对语义理解和常识推理是有效的,但粗粒度语义无法表示复杂的对象间的关系。认知语言学提供了表示复杂对象关系的方法,但由于其抽象性而难以应用于自然语言处理。文中提出一种基于认知语言学理论的自然语言语义表示方法。该方法使用意象图式表示语义网中对象节点间的关系,使用属性空间表示可以数值化的语义(如颜色)。在此基础上,将语义网和属性空间的构建和修改过程转化为语义操作序列,实现了句子语义的动态重构。相比于传统的基于语义网的语义表示方法,提出的方法能够表示动态关系,具有更强的推理能力。文中通过实例证明了这一方法的可行性。

Semantic network is valid to semantic understanding and common sense reasoning,but coarse-grained semantic is difficult to represent complex relationship between objects.Though cognitive linguistics provides approaches to represent complex object-relational,it's difficult to support natural language processing because of its abstract.This paper presented a method of natural language semantic representation based on cognitive linguistics.The method uses image schema to represent the relationship between object nodes in semantic network,and attribute spaces to represent the semantic of object which can be digitized(such as color).On this basis,we transformed the building and modifying process on semantic network and attribute space into a sequence of semantic operations,so that we could reconstruct the semantics of sentences dynamically.Compared to the traditional method of semantic representation based on semantic network,the proposed method can express dynamic relationship and has stronger reasoning ability.The paper gave examples to prove the feasibility of this approach.