Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Discourse and Information Structure
Journal of Logic, Language and Information
Tagging of very large corpora: topic-focus articulation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Producing contextually appropriate intonation in an information-state based dialogue system
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Information based intonation synthesis
HLT '94 Proceedings of the workshop on Human Language Technology
Learning information status of discourse entities
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
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This paper investigates automatic identification of Information Structure (IS) in texts. The experiments use the Prague Dependency Treebank which is annotated with IS following the Praguian approach of Topic Focus Articulation. We automatically detect t(opic) and f(ocus), using node attributes from the treebank as basic features and derived features inspired by the annotation guidelines. We present the performance of decision trees (C4.5), maximum entropy, and rule induction (RIPPER) classifiers on all tectogrammatical nodes. We compare the results against a baseline system that always assigns f(ocus) and against a rule-based system. The best system achieves an accuracy of 90.69%, which is a 44.73% improvement over the baseline (62.66%).