Information Processing and Management: an International Journal
The syntactic process
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Three generative, lexicalised models for statistical parsing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Efficient normal-form parsing for combinatory categorial grammar
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Generative models for statistical parsing with Combinatory Categorial Grammar
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Log-linear models for wide-coverage CCG parsing
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
TeachCL '08 Proceedings of the Third Workshop on Issues in Teaching Computational Linguistics
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This paper proposes a novel approach to the induction of Combinatory Categorial Grammars (CCGs) by their potential affinity with the Genetic Algorithms (GAs). Specifically, CCGs utilize a rich yet compact notation for lexical categories, which combine with relatively few grammatical rules, presumed universal. Thus, the search for a CCG consists in large part in a search for the appropriate categories for the data-set's lexical items. We present and evaluates a system utilizing a simple GA to successively search and improve on such assignments. The fitness of categorial-assignments is approximated by the coverage of the resulting grammar on the data-set itself, and candidate solutions are updated via the standard GA techniques of reproduction, crossover and mutation.