Named Entity Extraction from Noisy Input: Speech and OCR (2000)
| Venue: | In Proceedings of the 6th Applied Natural Language Processing Conference |
| Citations: | 16 - 0 self |
BibTeX
@INPROCEEDINGS{Miller00namedentity,
author = {David Miller and Sean Boisen and Richard Schwartz and Rebecca Stone and Ralph Weischedel},
title = {Named Entity Extraction from Noisy Input: Speech and OCR},
booktitle = {In Proceedings of the 6th Applied Natural Language Processing Conference},
year = {2000},
pages = {316--324}
}
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Abstract
In this paper, we analyze the performance of name finding in the context of a variety of automatic speech recognition (ASR) systems and in the context of one optical character recognition (OCR) system. We explore the effects of word error rate from ASR and OCR, performance as a function of the amount of training data, and for speech, the effect of out-of-vocabulary errors and the loss of punctuation and mixed case 1
Citations
| 270 | An Algorithm that Learns What’s in a Name - Bikel, Schwartz, et al. - 1999 |
| 238 | Nymble: a High-Performance Learning Name-finder - Bikel, Miller, et al. - 1997 |
| 27 | Muc-7 named entity task definition (version - Chinchor - 1998 |
| 20 | Named entity extraction from speech: Approach and results using the textpro system - Appelt, Martin - 1999 |
| 7 | Information Extraction from Broadcast News Speech Data - Palmer, Burger, et al. - 1999 |
| 4 | A Script-Independent Methodology for Optical Character - Makhoul, Schwartz, et al. - 1998 |
| 3 | Mizoguchi,R.,Bourdeau,J.UsingOntologicalEngin eering toOvercomeCommonAI-EDProblems,International - Chincor, Robinson, et al. - 1998 |
| 1 | Wu,H.AReferenceArchitectureforAdaptiveHyperm edia Applications,doctoralthesis,EindhovenUniversity of Technology,TheNetherlands,ISBN90-386-0572-2 - unknown authors - 1998 |
