A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
A TAG-based noisy channel model of speech repairs
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Effective use of prosody in parsing conversational speech
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Partial parse selection for robust deep processing
DeepLP '07 Proceedings of the Workshop on Deep Linguistic Processing
Reconstructing false start errors in spontaneous speech text
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Exploring features for identifying edited regions in disfluent sentences
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Reconstructing spontaneous speech
Reconstructing spontaneous speech
Correction detection and error type selection as an ESL educational aid
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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While speaking spontaneously, speakers often make errors such as self-correction or false starts which interfere with the successful application of natural language processing techniques like summarization and machine translation to this data. There is active work on reconstructing this errorful data into a clean and fluent transcript by identifying and removing these simple errors. Previous research has approximated the potential benefit of conducting word-level reconstruction of simple errors only on those sentences known to have errors. In this work, we explore new approaches for automatically identifying speaker construction errors on the utterance level, and quantify the impact that this initial step has on word- and sentence-level reconstruction accuracy.