Topic discovery based on text mining techniques

Authors:
Aurora Pons-Porrata;Rafael Berlanga-Llavori;José Ruiz-Shulcloper
Affiliations:
Center of Pattern Recognition and Data Mining, Universidad de Oriente, Patricio Lumumba s/n, Santiago de Cuba 90500, Cuba;Computer Science, Universitat Jaume I, Avda. Vicent Sos Banyat, Campus del Riu Sec s/n, E-12071 Castellón, Spain;Advanced Technologies Application Center, 7ma, No. 21812, Siboney, C. Habana, Cuba
Venue:
Information Processing and Management: an International Journal
Year:
2007

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Abstract

In this paper, we present a topic discovery system aimed to reveal the implicit knowledge present in news streams. This knowledge is expressed as a hierarchy of topic/subtopics, where each topic contains the set of documents that are related to it and a summary extracted from these documents. Summaries so built are useful to browse and select topics of interest from the generated hierarchies. Our proposal consists of a new incremental hierarchical clustering algorithm, which combines both partitional and agglomerative approaches, taking the main benefits from them. Finally, a new summarization method based on Testor Theory has been proposed to build the topic summaries. Experimental results in the TDT2 collection demonstrate its usefulness and effectiveness not only as a topic detection system, but also as a classification and summarization tool.