Abstract
We present an analysis of parser performance on speech data, comparing word type and token frequency distributions with written data, and evaluating parse accuracy by length of input string. We find that parser performance tends to deteriorate with increasing length of string, more so for spoken than for written texts. We train an alternative parsing model with added speech data and demonstrate improvements in accuracy on speech-units, with no deterioration in performance on written text.
Cite
CITATION STYLE
Caines, A., McCarthy, M., & Buttery, P. (2017). Parsing transcripts of speech. In EMNLP 2017 - 1st Workshop on Speech-Centric Natural Language Processing, SCNLP 2017 - Proceedings of the Workshop (pp. 27–36). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w17-4604
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