A methodology for extrinsically evaluating information extraction performance

Authors:
Michael Crystal;Alex Baron;Katherine Godfrey;Linnea Micciulla;Yvette Tenney;Ralph Weischedel
Affiliations:
BBN Technologies, Cambridge, MA;BBN Technologies, Cambridge, MA;BBN Technologies, Cambridge, MA;BBN Technologies, Cambridge, MA;BBN Technologies, Cambridge, MA;BBN Technologies, Cambridge, MA
Venue:
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Year:
2005

Citing 2
Cited 1

A novel use of statistical parsing to extract information from text

NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
FactBrowser demonstration

HLT '01 Proceedings of the first international conference on Human language technology research

A new benchmark dataset with production methodology for short text semantic similarity algorithms

ACM Transactions on Speech and Language Processing (TSLP)

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper reports a preliminary study addressing two challenges in measuring the effectiveness of information extraction (IE) technology:• Developing a methodology for extrinsic evaluation of IE; and,• Estimating the impact of improving IE technology on the ability to perform an application task.The methodology described can be employed for further controlled experiments regarding information extraction.