Issue No. 01 - January (2012 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.250
Jeremy H. Wright , AT&T Labs - Research, Florham Park
John Grothendieck , Raytheon BBN Technologies
Text streams are ubiquitous and contain a wealth of information, but are typically orders of magnitude too large in scale for comprehensive human inspection. There is a need for tools that can detect and group changes occurring within text streams and substreams, in order to find, structure, and summarize these changes for presentation to human analysts. This paper describes a procedure for efficiently finding step changes, trends, bursts, and cyclic changes affecting frequencies of words, or more general lexical items, within streams of documents which may be optionally labeled with metadata. The common phenomenon of over-dispersion is accommodated using mixture distributions. A streaming implementation is described which can process data from a continuous feed. Anomalies can be detected, grouped, and rendered visually for human comprehension.
Statistical software, modeling structured, textual and multimedia data, text mining.
Jeremy H. Wright, John Grothendieck, "CoCITe—Coordinating Changes in Text", IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 15-29, January 2012, doi:10.1109/TKDE.2010.250