Abstract
This article describes a novel approach to probabilistic LR-parsing of spontaneously spoken utterances developed in Verbmobil. It extends the use of context knowledge within the probabilistic model of the parser and improves its output by applying tree transformation rules learned from corpora. The parser was developed for German, English and Japanese and achieves more than 90% Labeled Recall/Precision on parsed Verbmobil utterances.
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Ruland, T. (2000). Probabilistic LR-Parsing with Symbolic Postprocessing. In: Wahlster, W. (eds) Verbmobil: Foundations of Speech-to-Speech Translation. Artificial Intelligence. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04230-4_12
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DOI: https://doi.org/10.1007/978-3-662-04230-4_12
Publisher Name: Springer, Berlin, Heidelberg
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