Detecting Optional Arguments of Verbs
András Kornai, Dávid Márk Nemeskey, Gábor Recski · 2016
We propose a novel method for detecting optional arguments of Hungarian verbs using only positive data.We introduce a custom variant of collexeme analysis that explicitly models the noise in verb frames.Our method is, for the most part, unsupervised: we use the spectral clustering algorithm described in Brew and Schulte in Walde (2002) to build a noise model from a short, manually verified seed list of verbs.We experimented with both raw count-and context-based clusterings and found their performance almost identical.The code for our algorithm and the frame list are freely available at http://hlt.bme.hu/en/resources/tade.