Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Introduction to Algorithms
Information fusion for multidocument summarization: paraphrasing and generation
Information fusion for multidocument summarization: paraphrasing and generation
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Computational Linguistics
Decoding complexity in word-replacement translation models
Computational Linguistics
A foundation for general-purpose natural language generation: sentence realization using probabilistic models of language
Two-level, many-paths generation
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Journal of Artificial Intelligence Research
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Discourse generation using utility-trained coherence models
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Experiments on Generating Questions About Facts
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Seed and Grow: augmenting statistically generated summary sentences using schematic word patterns
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Spanning tree approaches for statistical sentence generation
Empirical methods in natural language generation
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We describe a new sentence realization framework for text-to-text applications. This framework uses IDL-expressions as a representation formalism, and a generation mechanism based on algorithms for intersecting IDL-expressions with probabilistic language models. We present both theoretical and empirical results concerning the correctness and efficiency of these algorithms.