Statistical Models for Text Segmentation

Statistical Models for Text Segmentation (1999)

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by Doug Beeferman Adam , Adam Berger , John Lafferty

Venue:	Machine Learning
Citations:	1 - 0 self

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Datum	Value	Source
TITLE	Statistical Models for Text Segmentation	user correction - Legacy Corrections
AUTHOR NAME	Doug Beeferman Adam	user correction - Legacy Corrections
AUTHOR AFFIL	School of Computer Science; Carnegie Mellon University	user correction - Legacy Corrections
AUTHOR ADDR	Pittsburgh, PA 15213 USA	user correction - Legacy Corrections
AUTHOR NAME	Adam Berger	user correction - Legacy Corrections
AUTHOR AFFIL	School of Computer Science; Carnegie Mellon University	user correction - Legacy Corrections
AUTHOR ADDR	Pittsburgh, PA 15213 USA	user correction - Legacy Corrections
AUTHOR NAME	John Lafferty	user correction - Legacy Corrections
AUTHOR AFFIL	School of Computer Science; Carnegie Mellon University	user correction - Legacy Corrections
AUTHOR ADDR	Pittsburgh, PA 15213 USA	user correction - Legacy Corrections
ABSTRACT	. This paper introduces a new statistical approach to automatically partitioning text into coherent segments. The approach is based on a technique that incrementally builds an exponential model to extract features that are correlated with the presence of boundaries in labeled training text. The models use two classes of features: topicality features that use adaptive language models in a novel way to detect broad changes of topic, and cue-word features that detect occurrences of specific words, whichmay be domain-specific, that tend to be used near segment boundaries. Assessment of our approachonquantitative and qualitative grounds demonstrates its effectiveness in twovery different domains, Wall Street Journal news articles and television broadcast news story transcripts. Quantitative results on these domains are presented using a new probabilistically motivated error metric, whichcombines precision and recall in a natural and flexible way. This metric is used to make a quantitative ...	user correction - Legacy Corrections
YEAR	1999	INFERENCE
VENUE	Machine Learning	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	177--210	INFERENCE
VOLUME	34	INFERENCE
CITATIONS	25 found	ParsCit 1.0