Applications of finite automata representing large vocabularies
Software—Practice & Experience
Statistical methods for speech recognition
Statistical methods for speech recognition
Communications of the ACM
Experiments with Automata Compression
CIAA '00 Revised Papers from the 5th International Conference on Implementation and Application of Automata
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
A maximum entropy/minimum divergence translation model
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
The order of prenominal adjectives in natural language generation
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
An unsupervised approach to prepositional phrase attachment using contextually similar words
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Finite state tools for natural language processing
Proceedings of the COLING-2000 Workshop on Using Toolsets and Architectures To Build NLP Systems
Position Models and Language Modeling
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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A technique for compact representation of language models in Natural Language Processing is presented. After a brief review of the motivations for a more compact representation of such language models, it is shown how finite-state automata can be used to compactly represent such language models. The technique can be seen as an application and extension of perfect hashing by means of finite-state automata. Preliminary practical experiments indicate that the technique yields considerable and important space savings of up to 90% in practice.