Abstract
We present a supervised learning method for verbal valency frame detection and selection, i.e., a specific kind of word sense disambiguation for verbs based on subcategorization information, which amounts to detecting mentions of events in text. We use the rich dependency annotation present in the Prague Dependency Treebanks for Czech and English, taking advantage of several analysis tools (taggers, parsers) developed on these datasets previously.
The frame selection is based on manually created lexicons accompanying these treebanks, namely on PDT-Vallex for Czech and EngVallex for English. The results show that verbal predicate detection is easier for Czech, but in the subsequent frame selection task, better results have been achieved for English.