Paper Info
Title
A System for new event detection
Abstract
We present a new method and system for performing the New Event Detection task, i.e., in one or multiple streams of news stories, all stories on a previously unseen (new) event are marked. The method is based on an incremental TF-IDF model. Our extensions include: generation of source-specific models, similarity score normalization based on document-specific averages, similarity score normalization based on source-pair specific averages, term reweighting based on inverse event frequencies, and segmentation of the documents. We also report on extensions that did not improve results. The system performs very well on TDT3 and TDT4 test data and scored second in the TDT-2002 evaluation.
Year
DOI
Venue
2003
10.1145/860435.860495
SIGIR
Keywords
Field
DocType
incremental tf-idf model,document-specific average,multiple stream,tdt4 test data,new event detection,new event detection task,inverse event frequency,similarity score normalization,new method,news story,tdt-2002 evaluation,system performance
Data mining,Normalization (statistics),Pattern recognition,Computer science,Segmentation,Speech recognition,Test data,Artificial intelligence
Conference
ISBN
Citations 
PageRank 
1-58113-646-3
110
6.23
References 
Authors
7
3
Search Limit
100110
Name
Order
Citations
PageRank
Thorsten Brants11938190.33
Francine Chen21218153.96
Ayman Farahat324418.07