Artificial intelligence
Natural Language Processing in LISP: An Introduction to Computational Linguistics
Natural Language Processing in LISP: An Introduction to Computational Linguistics
Recovery strategies for parsing extragrammatical language
Computational Linguistics - Special issue on ill-formed input
Meta-rules as a basis for processing ill-formed input
Computational Linguistics - Special issue on ill-formed input
Deterministic parsing of syntactic non-fluencies
ACL '83 Proceedings of the 21st annual meeting on Association for Computational Linguistics
Some chart-based techniques for parsing ill-formed input
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Efficiency, robustness and accuracy in Picky chart parsing
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
COLING '88 Proceedings of the 12th conference on Computational linguistics - Volume 1
COLING '88 Proceedings of the 12th conference on Computational linguistics - Volume 2
Surface-marker-based dialog modelling: A progress report on the MAREDI project
Natural Language Engineering
Integrated control of chart items for error repair
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Users' Perception and Usability Study of a Parser for Headings
APCHI '08 Proceedings of the 8th Asia-Pacific conference on Computer-Human Interaction
Semi-supervised CCG lexicon extension
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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A new chart-based technique for parsing ill-formed input is proposed. This can process sentences with unknown/misspelled words, omitted words or extraneous words. This generalized parsing strategy is, similar to Mellish's, based on an active chart parser, and shares the many advantages of Mellish's technique. It is based on pure syntactic knowledge, it is independent of all grammars, and it does not slow down the original parsing operation if there is no ill-formedness. However, unlike Mellish's technique, it doesn't employ any complicated heuristic parameters. There are two key points. First, instead of using a unified or interleaved process for finding errors and correcting them, we separate the initial error detection stage from the other stages and adopt a version of bi-directional parsing. This effectively prunes the search space. Second, it employs normal top-down parsing, in which each parsing state reflects the global context, instead of topdown chart parsing. This enables the technique to determine the global plausibility of candidates easily, based on an admissible A search. The proposed strategy could enumerate all possible minimal-penalty solutions in just 4 times the time taken to parse the correct sentences.