The syntactic process
Ambiguity management in natural language generation
Ambiguity management in natural language generation
Forest-based statistical sentence generation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Two-level, many-paths generation
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Exploiting a probabilistic hierarchical model for generation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Factored language models and generalized parallel backoff
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
The order of prenominal adjectives in natural language generation
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Techniques for text planning with XSLT
NLPXML '04 Proceeedings of the Workshop on NLP and XML (NLPXML-2004): RDF/RDFS and OWL in Language Technology
Avoiding repetition in generated text
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
Interleaved preparation and output in the COMIC fission module
Software '05 Proceedings of the Workshop on Software
Design and Implementation of Web-based Discharge Summary Note Based on Service-Oriented Architecture
Journal of Medical Systems
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We present an extensible API for integrating language modeling and realization, describing its design and efficient implementation in the OpenCCG surface realizer. With OpenCCG, language models may be used to select realizations with preferred word orders, promote alignment with a conversational partner, avoid repetitive language use, and increase the speed of the best-first anytime search. The API enables a variety of n-gram models to be easily combined and used in conjunction with appropriate edge pruning strategies. The n-gram models may be of any order, operate in reverse ("right-to-left"), and selectively replace certain words with their semantic classes. Factored language models with generalized backoff may also be employed, over words represented as bundles of factors such as form, pitch accent, stem, part of speech, supertag, and semantic class.