Building term hierarchies using graph-based clustering

Mark Hloch, Markus Van Meegen, M. Kubek, H. Unger
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引用次数: 0

Abstract

Classical tasks of a librarian, such as screening and categorizing new documents based on their content, are increasingly replaced by search engines or through the use of cataloging software. A first overview of a corpus topical orientation can be achieved by combining graph-based search engines and clustering methods. Existing classical clustering methods, however, often require an a priori specification of the desired number of clusters to be output and do not consider term relationships in graphs, which is deficient from a practical point of view. Therefore, fully unsupervised graph-based clustering approaches at the term level offer new possibilities that mitigate these shortcomings. Within this work, a set of novel graph-based clustering algorithms have been developed. The hierarchical clustering algorithm (HCA) forms term hierarchies by iteratively isolating nodes of a given co-occurrence graph based on the evaluation of the edge weight between the nodes. Based on the co-occurrence graph inherent relationships of terms, a new graph is built agglomerative forming individual term clusters of related terms. The feasibility of the outlined methods for text analysis is shown.
使用基于图的聚类构建术语层次结构
图书管理员的经典任务,如根据内容对新文档进行筛选和分类,越来越多地被搜索引擎或编目软件所取代。通过结合基于图的搜索引擎和聚类方法,可以实现语料库主题方向的第一个概述。然而,现有的经典聚类方法通常需要对输出的期望聚类数量进行先验说明,并且不考虑图中的项关系,这从实际的角度来看是不足的。因此,在术语级别上,完全无监督的基于图的聚类方法为减轻这些缺点提供了新的可能性。在这项工作中,开发了一套新颖的基于图的聚类算法。层次聚类算法(HCA)基于节点间边权的评估,通过迭代分离给定共现图的节点,形成词层次。基于词间的共现图内在关系,构建了由相关词组成的聚类图。说明了本文提出的文本分析方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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