Abstract
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.- Anthology ID:
- L16-1448
- Volume:
- Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
- Month:
- May
- Year:
- 2016
- Address:
- Portorož, Slovenia
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 2815–2818
- Language:
- URL:
- https://aclanthology.org/L16-1448
- DOI:
- Cite (ACL):
- András Kornai, Dávid Márk Nemeskey, and Gábor Recski. 2016. Detecting Optional Arguments of Verbs. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 2815–2818, Portorož, Slovenia. European Language Resources Association (ELRA).
- Cite (Informal):
- Detecting Optional Arguments of Verbs (Kornai et al., LREC 2016)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/L16-1448.pdf