汉语逻辑补足义标注框架研究

张坤丽,韩英杰,贾玉祥,穆玲玲,穗志方,昝红英

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中文信息学报 ›› 2019, Vol. 33 ›› Issue (2) : 8-16.
语言分析与计算

汉语逻辑补足义标注框架研究

  • 张坤丽1,韩英杰1,贾玉祥1,穆玲玲1,穗志方2,昝红英1
作者信息 +

Semantic Annotation Framework for Chinese Logical Complement

  • ZHANG Kunli1, HAN Yingjie1, JIA Yuxiang1, MU Lingling1, SUI Zhifang2, ZAN Hongying1
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摘要

逻辑补足义是指附加在以谓词为中心的基本命题成分之上的否定、程度、时体、模态和语气等, 具体表现为逻辑语义算子对谓词的语义约束关系,是基本命题成分所表达语义关系的有效补充。在句子中,逻辑补足义所表达的语义是句子深度语义理解的重要层面。该文以深层语义理解为目标,在逻辑补足义已有的研究基础上,建立了否定、程度、时体和语气分类体系,构建了相应的算子词典;制定标注规范,对已经标注了基本命题义语义角色的句子进行各类逻辑补足义的标注;最后,对标注的结果进行统计并对标注过程中出现的问题进行了分析。

Abstract

Logical complement semantics is referred to as the meaning expressed by elements of negation, degree, tense and aspect, modality and mood that are attached to a basic predicate-centered proposition in a sentence. It is embodied as the semantic constraint relation between logical semantic operators and the predicate. Logical complement semantics as an effective supplement of semantic relations expressed by elements in a basic proposition is important for deep understanding of sentence semantics. This paper proposes a Chinese logical complement semantic annotation framework for deep semantic understanding. Specifically, classification systems and operator dictionaries are constructed for representing negation, degree, tense and aspect, and mood based on existing research results. Annotation rules are established to annotate logic complement semantics for the sentences which have been tagged for basic propositional arguments. Finally, the statistics of annotation results is presented, and the problems in annotation process are also analyzed.

关键词

逻辑补足义 / 否定 / 程度 / 时体 / 语气 / 标注框架

Key words

logical complement semantics / negation / degree / tense and aspect / mood / annotation framework

引用本文

导出引用
张坤丽,韩英杰,贾玉祥,穆玲玲,穗志方,昝红英. 汉语逻辑补足义标注框架研究. 中文信息学报. 2019, 33(2): 8-16
ZHANG Kunli, HAN Yingjie, JIA Yuxiang, MU Lingling, SUI Zhifang, ZAN Hongying. Semantic Annotation Framework for Chinese Logical Complement. Journal of Chinese Information Processing. 2019, 33(2): 8-16

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基金

国家重点基础研究发展计划(973计划)(2014CB340504);国家自然科学基金(61402419,60970083);国家社会科学基金(14BYY096);计算语言学教育部重点实验室开放课题项目;河南省科技厅科技攻关项目(172102210478)
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