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Conference Paper

Incremental Aspect Models for Mining Document Streams

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons76142

Sra,  S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Surendran, A., & Sra, S. (2006). Incremental Aspect Models for Mining Document Streams. Knowledge Discovery in Databases: PKDD 2006, 633-640.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D049-6
Abstract
In this paper we introduce a novel approach for incrementally building aspect models, and use it to dynamically discover underlying themes from document streams. Using the new approach we present an application which we call “query-line tracking” i.e., we automatically discover and summarize different themes or stories that appear over time, and that relate to a particular query. We present evaluation on news corpora to demonstrate the strength of our method for both query-line tracking, online indexing and clustering.