A statistical approach to machine translation
Computational Linguistics
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Building natural language generation systems
Building natural language generation systems
Lessons from a failure: generating tailored smoking cessation letters
Artificial Intelligence
Can nominal expressions achieve multiple goals?: an empirical study
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Evaluating algorithms for the generation of referring expressions using a balanced corpus
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
Building a semantically transparent corpus for the generation of referring expressions
INLG '06 Proceedings of the Fourth International Natural Language Generation Conference
Text-to-text surface realisation using dependency-tree replacement
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
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Natural Language Generation systems usually require substantial knowledge about the structure of the target language in order to perform the final task in the generation process --- the mapping from semantic representation to text known as surface realisation. Designing knowledge bases of this kind, typically represented as sets of grammar rules, may however become a costly, labour-intensive enterprise. In this work we take a statistical approach to surface realisation in which no linguistic knowledge is hard-coded, but rather trained automatically from large corpora. Results of a small experiment in the generation of referring expressions show significant levels of similarity between our (computer-generated) text and those produced by humans, besides the usual benefits commonly associated with statistical NLP such as low development costs, domain- and language-independency.