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

Teresa Lynn, Jennifer Foster, Mark Dras, Josef van Genabith

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

3 Citations (Scopus)

Abstract

We present a number of semi-supervised parsing 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.
Original languageEnglish
Title of host publicationSPMRL 2013
Subtitle of host publicationFourth Workshop on Statistical Parsing of Morphologically Rich Languages : proceedings of the the workshop
Place of PublicationStroudsburg, PA
PublisherAssociation for Computational Linguistics
Pages1-11
Number of pages11
ISBN (Print)9781937284978
Publication statusPublished - 2013
EventWorkshop on Statistical Parsing of Morphologically Rich Languages (4th : 2013) - Seattle, WA
Duration: 18 Oct 201321 Oct 2013

Workshop

WorkshopWorkshop on Statistical Parsing of Morphologically Rich Languages (4th : 2013)
CitySeattle, WA
Period18/10/1321/10/13

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  • Cite this

    Lynn, T., Foster, J., Dras, M., & van Genabith, J. (2013). Working with a small dataset - semi-supervised dependency parsing for Irish. In SPMRL 2013: Fourth Workshop on Statistical Parsing of Morphologically Rich Languages : proceedings of the the workshop (pp. 1-11). Stroudsburg, PA: Association for Computational Linguistics.