Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Generation that exploits corpus-based statistical knowledge
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Statistical decision-tree models for parsing
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Immediate-head parsing for language models
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Instance-based natural language generation
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
SPoT: a trainable sentence planner
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Statistical parsing with an automatically-extracted tree adjoining grammar
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Learning as search optimization: approximate large margin methods for structured prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
ETMTNLP '02 Proceedings of the ACL-02 Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics - Volume 1
Discriminative Reranking for Natural Language Parsing
Computational Linguistics
AI Magazine - Special issue on achieving human-level AI through integrated systems and research
Trainable sentence planning for complex information presentation in spoken dialog systems
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Robust PCFG-based generation using automatically acquired LFG approximations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Making grammar-based generation easier to deploy in dialogue systems
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
Evaluating coverage for large symbolic NLG grammars
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Making grammar-based generation easier to deploy in dialogue systems
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
Instance-based natural language generation
Natural Language Engineering
Text generation for Brazilian Portuguese: the surface realization task
YIWCALA '10 Proceedings of the NAACL HLT 2010 Young Investigators Workshop on Computational Approaches to Languages of the Americas
Dependency-based n-gram models for general purpose sentence realisation
Natural Language Engineering
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We present a technique that opens up grammar-based generation to a wider range of practical applications by dramatically reducing the development costs and linguistic expertise that are required. Our method infers the grammatical resources needed for generation from a set of declarative examples that link surface expressions directly to the application's available semantic representations. The same examples further serve to optimize a run-time search strategy that generates the best output that can be found within an application-specific time frame. Our method offers substantially lower development costs than hand-crafted grammars for application-specific NLG, while maintaining high output quality and diversity.