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作 者:杨春雷[1]
出 处:《数据分析与知识发现》2017年第11期1-11,共11页Data Analysis and Knowledge Discovery
基 金:国家社会科学基金项目"类型学视野下的汉语短语结构语法及其计算实现研究"(项目编号:16BYY136);上海市哲学社会科学规划课题"语言量化现象的多维度研究"(项目编号:2014BYY001);上海外国语大学规划基金项目"语言量化现象的多维度研究"(项目编号:2013XJGH023)的研究成果之一
摘 要:【目的】以面向语用消歧的量化约束条件系统(Quantifier Constraint System,QCS)为例,构建并实践从本体语言学到其计算实现的完整研究方法。【方法】首先,阐释QCS的原理,并介绍具体研究方法。第二,提出甄别有效约束条件和组建系统的标准,以及优化赋值的算法。第三,详细描写与量化相关的词类和语法规律。第四,使用类别描写语言(Type Description Language)对这些语言规律进行形式化描写,并基于"汉构"和Zhong[|]计算语法进行计算实现。【结果】运用该研究方法,可计算实现较为复杂的语言学现象(如量化、约束和前指解决方案等)。【结论】本文方法不仅有助于语言学的整体发展,还可为相关学科(如人工智能、深层语言处理等)提供语言学方面的技术支持。[Objective] This article tries to bridge ontological linguistic design and its computational implementation, taking quantification constraint system(QCS) for pragmatic disambiguation as an example. [Methods] First, this study explained the rationale of QCS, and introduced the new research methodology. Second, the author proposed the criteria to identify effective constraints and integrate them to a system, as well as the algorithm to optimize vote assignment. Third, it described the lexical and grammatical regularities pertinent to quantification. Fourth, this paper formulated regularities using type description language(TDL) and then implemented them based on two Chinese computational grammars, namely Man GO and Zhong[|]. [Results] This new method effectively processed complex linguistic phenomena(e.g., quantification, binding and anaphora). [Conclusions] The proposed method could accelerate the development of linguistics, which provides technical support for artificial intelligence and deep linguistic processing.
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