Learning information structure in the Prague treebank

Abstract

This paper investigates the automatic identification of aspects 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 show the performance of C4.5, Bagging, and Ripper classifiers on several classes of instances such as nouns and pronouns, only nouns, only pronouns. A baseline system assigning always f(ocus) has an F-score of 42.5%. Our best system obtains 82.04%.

Information structure and pauses in a corpus of spoken Danish

“…The interest for corpora annotated with information structure has been raised recently by several authors. Kruijff-Korbayová and Kruijff (2004) describe a method where a rich discourselevel annotation is used to investigate information structure, while both Postolache (2005) and Diderichsen and Elming (2005) study the application of machine learning to the problem of automatic identification of topic and focus. In this study, on the contrary, information structure is annotated manually, and the annotation is used to investigate the correlation between information structure tags and intra-clausal pauses.…”