考虑共现词距离和连通性的基于图的词聚类

Supaporn Simcharoen, H. Unger
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摘要

聚类是一种典型的自然语言处理方法。考虑不同因素的词聚类方法有几种。下面的文章介绍了两个因素,包括最近距离和共现的连通性。选取k-means和Chinese Whispers这两种经典聚类算法,比较它们的聚类质量。结果表明,提出的两种因子聚类算法的聚类质量都接近k-means聚类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Graph-based Word Clustering Considering the Distance and the Connectivity of a Co-occurrence
Word clustering is a typical method of natural language processing. Several approaches for word clustering have been developed which consider different factors. The following article presents two factors, including the closest distance and the connectivity of a co-occurrence. The classical clustering algorithms, including k-means and Chinese Whispers, are chosen to compare their cluster quality. The results show that the quality of both proposed clustering algorithms of these two factors is close to k-means clustering.
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