使用基于字符结构的关联度量来识别语义相关的单词对和文档标题

George W. Adamson, Jillian Boreham
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引用次数: 168

摘要

提出了一种基于词的特征结构的自动分类技术。骰子的相似系数是从字符串对中匹配图的数量计算出来的,并用于对字符串集进行聚类。从一个化学数据库中选择了一个单词样本,其中包含来自化学元素名称的某些词干。它们被成功地聚到语义相关的单词组中。每个聚类都由其所有成员派生的词根来表征。第二个来自《数学评论》的标题样本被聚集到定义良好的类中,这与《数学评论》的主题分组比较有利。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The use of an association measure based on character structure to identify semantically related pairs of words and document titles

An automatic classification technique has been developed, based on the character structure of words. Dice's Similarity Coefficient is computed from the number of matching digrams in pairs of character strings, and used to cluster sets of character strings. A sample of words from a chemical data base was chosen to contain certain stems derived from the names of chemical elements. They were successfully clustered into groups of semantically related words. Each cluster is characterised by the root word from which all its members are derived. A second sample of titles from Mathematical Reviews was clustered into well-defined classes, which compare favourably with the subject groupings of Mathematical Reviews.

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