A maximum entropy approach to natural language processing
Computational Linguistics
Applying natural language generation to indicative summarization
EWNLG '01 Proceedings of the 8th European workshop on Natural Language Generation - Volume 8
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Selection of natural-sounding referring expressions is useful in text generation and information summarization (Kan et al., 2001). We use discourse-level feature predicates in a maximum entropy classifier (Berger et al., 1996) with binary and n-class classification to select referring expressions from a list. We find that while mention-type n-class classification produces higher accuracy of type, binary classification of individual referring expressions helps to avoid use of awkward referring expressions.