Back
Incremental Aspect Models for Mining Document Streams
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.
@inproceedings{5220, title = {Incremental Aspect Models for Mining Document Streams}, journal = {Knowledge Discovery in Databases: PKDD 2006}, booktitle = {PKDD 2006}, 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.}, 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}, slug = {5220}, author = {Surendran, A. and Sra, S.}, month_numeric = {9} }