Random attributed graphs for statistical inference from content and context

Coping with Information Overload is a major challenge of the 21 st century. Huge volumes and varieties of multilingual data must be processed to extract salient information. Previous research has addressed automatic characterization of streaming content. However, information includes both content an...

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Bibliographic Details
Published in2010 IEEE International Conference on Acoustics, Speech and Signal Processing pp. 5430 - 5433
Main Authors Gorin, A L, Priebe, C E, Grothendieck, J
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2010
Subjects
change detection
Communication switching
Context modeling
coping with information overload
Data mining
Demography
Encoding
Humans
Natural languages
Parameter estimation
random attributed graphs
Signal processing
Speech
statistical inference
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Summary:Coping with Information Overload is a major challenge of the 21 st century. Huge volumes and varieties of multilingual data must be processed to extract salient information. Previous research has addressed automatic characterization of streaming content. However, information includes both content and associated meta-data, which humans deal with as a gestalt but computer systems often treat separately. Random attributed graphs provide an effective means to characterize and draw inferences from large volumes of language content plus associated meta-data. This paper describes these methods and their utility, with experimental proof-of-concept on the Switchboard and Enron corpora.
ISBN:9781424442959
1424442958
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2010.5494917