Document (#32917)

Author

Pons-Porrata, A.
Berlanga-Llavori, R.
Ruiz-Shulcloper, J.

Title

Topic discovery based on text mining techniques

Source

Information processing and management. 43(2007) no.3, S.752-768

Year

2007

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.

Footnote

Beitrag in: Special issue on Heterogeneous and Distributed IR

Theme

Data Mining

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5. Zhao, X.; Jin, P.; Yue, L.: Discovering topic time from web news (2015) 0.20

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