Punctuating speech for information extraction

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

doi:10.1109/ICASSP.2008.4518784

Punctuating speech for information extraction

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

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages	5013-5016
Number of pages	4
DOIs	https://doi.org/10.1109/ICASSP.2008.4518784
State	Published - 2008
Event	2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States Duration: Mar 31 2008 → Apr 4 2008

Publication series

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

Other

Other	2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Country/Territory	United States
City	Las Vegas, NV
Period	3/31/08 → 4/4/08

Keywords

Information extraction
Punctuation prediction
Speech

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2008.4518784

Cite this

Favre, B., Grishman, R., Hillard, D., Ji, H., Hakkani-Tür, D., & Ostendorf, M. (2008). Punctuating speech for information extraction. In 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP (pp. 5013-5016). Article 4518784 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2008.4518784

Punctuating speech for information extraction. / Favre, Benoit; Grishman, Ralph; Hillard, Dustin et al.
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP. 2008. p. 5013-5016 4518784 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Favre, B, Grishman, R, Hillard, D, Ji, H, Hakkani-Tür, D & Ostendorf, M 2008, Punctuating speech for information extraction. in 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP., 4518784, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 5013-5016, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, Las Vegas, NV, United States, 3/31/08. https://doi.org/10.1109/ICASSP.2008.4518784

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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.",

keywords = "Information extraction, Punctuation prediction, Speech",

author = "Benoit Favre and Ralph Grishman and Dustin Hillard and Heng Ji and Dilek Hakkani-T{\"u}r and Mari Ostendorf",

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N2 - 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.

AB - 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.

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ER -

Punctuating speech for information extraction

Abstract

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Keywords

ASJC Scopus subject areas

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