Efficient chart parsing of speech recognition candidates

In a spoken language system that uses the N-best interface between the continuous-speech recognition component and the natural language processing component, the role of the language processing component is to filter speech recognition candidates and to determine the semantic interpretation of the input. We propose as efficient chart parsing method of finding the most plausible sentence within the N-best candidate sentences and determining the semantic interpretation of the sentence, while avoiding the re-computation of the common substrings. We use syntactic and semantic constraints described in the unification-based framework and context-sensitive conditional probability CFG preferences to reorder the N-best candidates. In preliminary tests, our parser has been successful in reducing parsing steps by sharing common substrings and in selecting correct sentences first