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A content selection component determines which information should be conveyed in the output of a natural language generation system. We present an efficient method for automatically learning content selection rules from a corpus and its related database. Our modeling framework treats content selection as a collective classification problem, thus allowing us to capture contextual dependencies between input items. Experiments in a sports domain demonstrate that this approach achieves a substantial improvement over context-agnostic methods.
|Title of host publication||Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP),|
|Publisher||Association for Computational Linguistics|
|Number of pages||8|
|Publication status||Published - 2005|
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