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.
For online event analysis, Surendran and Sra REF proposed incrementally Built Aspect Models (BAMs) to dynamically discover new themes from document streams.
Incremental Aspect Models for Mining Document Streams
PKDD
Computer Science