An iterative unsupervised learning method for information distillation

Kamand Kamangar, Dilek Hakkani-Tür, Gokhan Tur, Michael Levit

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Information distillation techniques are used to analyze and interpret large volumes of speech and text archives in multiple languages and produce structured information of interest to the user. In this work, we propose an iterative un-supervised sentence extraction method to answer open-ended natural language queries about an event. The approach consists of finding the subset of sentences that are very likely to be relevant or irrelevant for the query from candidate documents, and iteratively training a classification model using these examples. Our results indicate that performance of the system may be improved by around 30% relative in terms of F-measure, by using the proposed method.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages4949-4952
Number of pages4
DOIs
StatePublished - 2008
Externally publishedYes
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: Mar 31 2008Apr 4 2008

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Country/TerritoryUnited States
CityLas Vegas, NV
Period3/31/084/4/08

Keywords

  • Information distillation
  • Machine learning
  • Question answering
  • Unsupervised learning

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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