BME HLT | Detecting Optional Arguments of Verbs

Detecting Optional Arguments of Verbs

2016

Link to paper

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.

Citation
@InProceedings{Kornai:2016c,
  author = {Andr\'as Kornai and D\'avid M\'ark Nemeskey and G\'abor Recski},
  title = {Detecting Optional Arguments of Verbs},
  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
  year = {2016},
  date = {23-28},
  location = {Portoro\v{z}, Slovenia},
  editor = {Nicoletta Calzolari and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-9-1},
 }