A context-sensitive model for probabilistic LR parsing of spoken language with transformation-based postprocessing
Tobias Ruland · 2000
This paper describes a hybrid approach to spontaneous speech parsing. The implemented parser uses an extended probabilistic LR parsing model with rich context and and its output is post-processed by a symbolic tree transformation routine that tries to eliminate systematic errors of the parser. The parser has been trained for three different languages and was successfully integrated in the Verbmobil speech-to-speech translation system. The parser achieves more than 90%/90% labeled precision/recall on parsed Verbmobil utterances while 3% of German and 5% of all English input cannot be parsed.