概念获取是自然语言理解领域中重要的研究课题。该文提出了一种基于汉语量词的名词概念描述方法,设计并实现了一个权重计算方案。通过聚类实验探索了量词对名词语义区分的作用和贡献,实验结果表明基于量词的名词概念表达方式是有效的,可以区分大部分名词概念。
Abstract
Concept acquisition from corpora has become increasingly important in NLP. This paper presents a new concept representation based on classifier words. Concepts are modeled as vectors with one component corresponding to each classifier word. We propose a weighting scheme that assigns each classifier word a weight in a concept. Then we conduct experiments to identify concept similarities via clustering, and the results show classifier words can categorize most concept classes.
关键词
概念获取 /
量名搭配 /
量词 /
聚类
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Key words
Concept acquisition /
classifier-noun collocation /
classifier words /
cluster
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参考文献
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脚注
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基金
国家自然科学基金(No.61300152)
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