Paper: Semi-supervised dependency parsing using generalized tri-training

ACL ID C10-1120
Title Semi-supervised dependency parsing using generalized tri-training
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010
Authors

Martins et al. (2008) presented what to the best of our knowledge still ranks as the best overall result on the CONLL- X Shared Task datasets. The paper shows how triads of stacked dependency parsers described in Martins et al. (2008) can label unlabeled data for each other in a way similar to co-training and produce end parsers that are significantly better than any of the stacked input parsers. We evaluate our system on five datasets from the CONLL-X Shared Task and ob- tain 10–20% error reductions, incl. the best reported results on four of them. We compare our approach to other semi- supervised learning algorithms.