Allan 1988

Allan James http://dl.acm.org/citation.cfm?id=290954&dl=ACM&coll=DL&CFID=119212228&CFTOKEN=52277574

Citation

@inproceedings{conf/sigir/AllanPL98,

 author    = {James Allan and
              Ron Papka and
              Victor Lavrenko},
 title     = {On-Line New Event Detection and Tracking},
 booktitle = {SIGIR},
 year      = {1998},
 pages     = {37-45},
 ee        = {http://doi.acm.org/10.1145/290941.290954},
 crossref  = {conf/sigir/98},

}

Abstract

Abstract We define and describe the related problems of new event detection and event tracking within a stream of broadcast news stories. We focus on a strict on-line setting-i.e., the system must make decisions about one story before looking at any subsequent stories. Our approach to detection uses a single pass clustering algorithm and a novel thresholding model that incorporates the properties of events as a major component. Our approach to tracking is similar to typical information filtering methods. We discuss the value of surprising features that have unusual occurrence characteristics, and briefly explore on-line adaptive filtering to handle evolving events in the news. New event detection and event tracking are part of the Topic Detection and Tracking (TDT) initiative.