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 semisupervised 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.
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Working with a small dataset - semi-supervised dependency parsing for Irish
Semantic Scholar · Computer Science · 2013
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 semisupervised 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.