Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Generalized probabilistic LR parsing of natural language (Corpora) with unification-based grammars
Computational Linguistics - Special issue on using large corpora: I
Machine Translation
Two-level, many-paths generation
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Multi-lingual translation of spontaneously spoken language in a limited domain
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Optimal ambiguity packing in context-free parsers with interleaved unification
New developments in parsing technology
Filling knowledge gaps in a broad coverage machine translation system
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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GLR is a recently developed robust version of the Generalized LR Parser [Tomita, 1986], that can parse almost any input sentence by ignoring unrecognizable parts of the sentence. On a given input sentence, the parser returns a collection of parses that correspond to maximal, or close to maximal, parsable subjsets of the original input. This paper describes recent work on developing an integrated heuristic scheme for selecting the parse that is deemed "best" from such a collection. We describe the heuristic measures used and their combination scheme. Preliminary results from experiments conducted on parsing speech recognized spontaneous speech are also reported.