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Working with a small dataset - semi-supervised dependency parsing for Irish

Lynn, Teresa, Foster, Jennifer orcid logoORCID: 0000-0002-7789-4853, Dras, Mark orcid logoORCID: 0000-0001-9908-7182 and van Genabith, Josef orcid logoORCID: 0000-0003-1322-7944 (2013) Working with a small dataset - semi-supervised dependency parsing for Irish. In: Fourth Workshop on Statistical Parsing of Morphologically Rich Languages, 18 Oct 2013, Seattle, WA. USA.

Abstract
We present a number of semi-supervised pars- ing experiments on the Irish language carried out using a small seed set of manually parsed trees and a larger, yet still relatively small, set of unlabelled sentences. We take two popular dependency parsers – one graph-based and one transition-based – and compare results for both. Results show that using semi- supervised learning in the form of self-training and co-training yields only very modest improvements in parsing accuracy. We also try to use morphological information in a targeted way and fail to see any improvements.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Uncontrolled Keywords:Language parsing
Subjects:Computer Science > Computational linguistics
Humanities > Irish language
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of SPMRL. . Association for Computational Linguistics.
Publisher:Association for Computational Linguistics
Official URL:http://aclweb.org/anthology//W/W13/#4900
Copyright Information:© 2013 ACL
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:y Science Foundation Ireland (Grant No. 07/CE/I1142) as part of the Centre for Next Generation Localisation (www.cngl.ie) at Dublin City University.
ID Code:19957
Deposited On:21 May 2014 10:04 by Jennifer Foster . Last Modified 19 Jan 2022 12:42
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