2021/10/14 by Gaëlle Candel, Candel, Gaëlle, David Naccache +1
Computer Science · Physics and Astronomy · #68T99 #Advanced Clustering Algorithms Research #Complex Network Analysis Techniques #FOS: Computer and information sciences #I.2.m #Information Retrieval (cs.IR) #Text and Document Classification Technologies #acm:68T99 #cs.IR #msc:68T99
paper · pdf · doi:10.48550/arxiv.2110.11079
15 pages, submitted and accepted to the 2021 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'21) - track ICAI21
arxiv created 2021/10/14 · openalex publication_date 2021/10/14 · arxiv updated 2021/10/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Tags are short sequences of words allowing to describe textual and non-texual resources such as as music, image or book. Tags could be used by machine information retrieval systems to access quickly a document. These tags can be used to build recommender systems to suggest similar items to a user. However, the number of tags per document is limited, and often distributed according to a Zipf law. In this paper, we propose a methodology to cluster tags into conceptual groups. Data are preprocessed to remove power-law effects and enhance the context of low-frequency words. Then, a hierarchical agglomerative co-clustering algorithm is proposed to group together the most related tags into clusters. The capabilities were evaluated on a sparse synthetic dataset and a real-world tag collection associated with scientific papers. The task being unsupervised, we propose some stopping criterion for selectecting an optimal partitioning.