Paper: Data-Driven Approaches For Information Structure Identification

ACL ID H05-1002
Title Data-Driven Approaches For Information Structure Identification
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
Authors

This paper investigates automatic identi- fication 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 auto- matically detect t(opic) and f(ocus), us- ing node attributes from the treebank as basic features and derived features in- spired by the annotation guidelines. We present the performance of decision trees (C4.5), maximum entropy, and rule in- duction (RIPPER) classifiers on all tec- togrammatical nodes. We compare the re- sults against a baseline system that always assigns f(ocus) and against a rule-based system. The best system achieves an ac- curacy of 90.69%, which is a 44.73% im- provement over the baseline (62.66%).