从图词中提取序列聚类文本文档

M. M. Fazal, M. Rafi
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引用次数: 1

摘要

文档聚类是一种无监督的机器学习技术,它将大量文档组织成较小的、主题同质的、有意义的子集合(簇)。传统的文档聚类方法使用从文档中提取的特征,如词(术语)、短语、序列和主题作为聚类过程的描述符。这些特性不考虑用于在文档中传达上下文信息的不同单词之间的关系。近年来,词图法被引入到信息研究中;这种方法通过从文档中出现的单词构建单词图来解决独立性假设的问题。因此,单词之间的关系在表示中被捕获。它是一个un[1]加权有向图,其顶点表示唯一项,其边表示项之间的共现。通过使用带有文档文本的大小= 3的滑动窗口来简化表示。本文使用从文档的graph[1]of-word中提取的基于序列的文档表示。在两个文档之间的公共序列上定义相似性度量。该方法在标准文本挖掘数据集上进行了实现和测试。一系列实验表明,该方法在聚类度量方面优于传统方法,如纯度、熵和F-Score。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clustering textual documents by extracting sequence from word-of-graph
Document clustering is an unsupervised machine learning technique that organizes a large collection of documents into smaller, topic homogenous, meaningful sub-collections (clusters). Traditional document clustering approaches use extracted features like: word (term), phrases, sequences and topics from the documents as descriptors for clustering process. These features do not consider the relationship among different words that are used to convey the contextual information within the document. Recently, Graph-of-Word approach is introduced in information research; this approach addresses the problem of independence assumption by building a graph of word from the words that appeared in a document. Hence, the relationships among words are captured in the representation. It is an un[1]weighted directed graph whose vertices represent unique terms and whose edges represent co-occurrences between the terms. The representation is simplified by using a sliding window of size = 3 with the text of the document. This paper uses a sequence based-representation of document that is extracted from graph[1]of-word of the document. A similarity measure is defined over the common sequences between two documents. The proposed approach is implemented and tested on standard text mining datasets. A series of experiments reveal that the proposed approach outperforms the traditional approaches on clustering measures like: Purity, Entropy and F-Score.
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