Abstract
This paper describes a time-series model for parsing transcribed speech containing disfluencies. This model differs from previous parsers in its explicit modeling of a buffer of recent words, which allows it to recognize repairs more easily due to the frequent overlap in words between errors and their repairs. The parser implementing this model is evaluated on the standard Switchboard transcribed speech parsing task for overall parsing accuracy and edited word detection. © 2009 ACL and AFNLP.
Cite
CITATION STYLE
Miller, T. (2009). Word buffering models for improved speech repair parsing. In EMNLP 2009 - Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: A Meeting of SIGDAT, a Special Interest Group of ACL, Held in Conjunction with ACL-IJCNLP 2009 (pp. 737–745). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1699571.1699609
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