NDLI: Supervised and unsupervised feature selection for inferring social nature of telephone conversations from their content

Content Provider	IEEE Xplore Digital Library
Author	Stark, A. Shafran, I. Kaye, J.
Copyright Year	2011
Description	Author affiliation: Biomedical Engineering, OHSU, Portland, USA (Stark, A.; Shafran, I.; Kaye, J.)
Abstract	The ability to reliably infer the nature of telephone conversations opens up a variety of applications, ranging from designing context-sensitive user interfaces on smartphones, to providing new tools for social psychologists and social scientists to study and understand social life of different subpopulations within different contexts. Using a unique corpus of everyday telephone conversations collected from eight residences over the duration of a year, we investigate the utility of popular features, extracted solely from the content, in classifying business-oriented calls from others. Through feature selection experiments, we find that the discrimination can be performed robustly for a majority of the calls using a small set of features. Remarkably, features learned from unsupervised methods, specifically latent Dirichlet allocation, perform almost as well as with as those from supervised methods. The unsupervised clusters learned in this task shows promise of finer grain inference of social nature of telephone conversations.
Starting Page	449
Ending Page	454
File Size	180258
Page Count	6
File Format	PDF
ISBN	9781467303651
e-ISBN	9781467303675
e-ISBN	9781467303668
DOI	10.1109/ASRU.2011.6163973
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Dictionaries Training Feature extraction Vectors Support vector machines Business
Content Type	Text
Resource Type	Article

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