Using Fuzzy Logic to Leverage HTML Markup for Web Page Representation
Alberto P. Garc\'ia-Plaza, V\'ictor Fresno, Raquel Mart\'inez, and Arkaitz Zubiaga

TL;DR
This paper introduces AFCC, a fuzzy logic-based method that leverages HTML structure to improve web page clustering by identifying the most representative words, outperforming traditional TF-IDF in various datasets.
Contribution
The paper presents a novel fuzzy term weighing approach that exploits HTML markup to enhance document representation for clustering, adapting to different dataset feature distributions.
Findings
AFCC outperforms TF-IDF in web page clustering tasks.
The fuzzy combination approach adapts well to datasets with varying feature distributions.
Exploiting HTML structure improves the identification of representative words.
Abstract
The selection of a suitable document representation approach plays a crucial role in the performance of a document clustering task. Being able to pick out representative words within a document can lead to substantial improvements in document clustering. In the case of web documents, the HTML markup that defines the layout of the content provides additional structural information that can be further exploited to identify representative words. In this paper we introduce a fuzzy term weighing approach that makes the most of the HTML structure for document clustering. We set forth and build on the hypothesis that a good representation can take advantage of how humans skim through documents to extract the most representative words. The authors of web pages make use of HTML tags to convey the most important message of a web page through page elements that attract the readers' attention, such…
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