Punctuating speech for information extraction

Benoit Favre, Ralph Grishman, Dustin Hillard, Heng Ji, Dilek Hakkani-Tür, Mari Ostendorf

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

Abstract

This paper studies the effect of automatic sentence boundary detection and comma prediction on entity and relation extraction in speech. We show that punctuating the machine generated transcript according to maximum F-measure of period and comma annotation results in suboptimal information extraction. Precisely, period and comma decision thresholds can be chosen in order to improve the entity value score and the relation value score by 4% relative. Error analysis shows that preventing noun-phrase splitting by generating longer sentences and fewer commas can be harmful for IE performance. Indeed, it seems that missed punctuation allows syntactic parsers to merge noun-phrases and prevent the extraction of correct information.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages5013-5016
Number of pages4
DOIs
StatePublished - 2008
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
CountryUnited States
CityLas Vegas, NV
Period3/31/084/4/08

Keywords

  • Information extraction
  • Punctuation prediction
  • Speech

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Punctuating speech for information extraction'. Together they form a unique fingerprint.

Cite this