基于功能连接词的隐式篇章关系推理

功能连接词是一种直接表述篇章单元内部语义关系、结构特性和语境发展趋势的词特征。借助功能连接词的这一优势,该文提出一种基于功能连接词的隐式篇章关系推理方法。该方法首先挖掘词级与短语级的功能连接词,划分功能连接词的篇章关系类别;其次,为每个功能连接词构建概念模型,借以描述由功能连接词连接的论元属性,并建立论元概念与篇章关系的映射体系;最后,利用统计策略识别待测论元的概念模型,并借助“概念—关系”映射体系,实现隐式篇章语义关系推理。实验结果显示,该文基于功能连接词构建概念模型的推理方法,相较于现有的基于监督学习的分类方法,系统性能获得显著提升。

Predicting Implicit Discourse Relation Based on Functional Connective

The functional connective is a word feature that directly expresses interior semantic relations, structure characteristics and the development trend of context of discourse units. Based on the functional connective, this paper puts forward a kind of methods for predicting relations of implicit discourse. First, this method mines functional connectives at the word and phraselevel, and divides the discourse relationcategory of functional connectives. Secondly, it buildsthe concept model for each type of functional connectives to describe argument attributes connected by functional connectives,and establishes the mapping system between argument concepts and discourse relations; Finally, the predictions of the implicit discoursesemantic relationis realized by statistical strategy to recognize conceptual model of argument and with “concept-relations” mapping system. The experimental results show that, the predicting method byconstructing concept model based on functional connectives, got the significant performance improvementscompared to the existing classification method based on supervised learning.