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Incremental Aspect Models for Mining Document Streams


Conference Paper


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.

Author(s): Surendran, A. and Sra, S.
Book Title: PKDD 2006
Journal: Knowledge Discovery in Databases: PKDD 2006
Pages: 633-640
Year: 2006
Month: September
Day: 0
Editors: F{\"u}rnkranz, J. , T. Scheffer, M. Spiliopoulou
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1007/11871637_65
Event Name: 10th European Conference on Principles and Practice of Knowledge Discovery in Databases
Event Place: Berlin, Germany

Address: Berlin, Germany
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {Incremental Aspect Models for Mining Document Streams},
  author = {Surendran, A. and Sra, S.},
  journal = {Knowledge Discovery in Databases: PKDD 2006},
  booktitle = {PKDD 2006},
  pages = {633-640},
  editors = {F{\"u}rnkranz, J. , T. Scheffer, M. Spiliopoulou},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = sep,
  year = {2006},
  month_numeric = {9}