Semi-supervised dependency parsing using generalized tri-training

TY - GEN

T1 - Semi-supervised dependency parsing using generalized tri-training

AU - Søgaard, Anders Østerskov

AU - Rishøj, Christian

PY - 2010

Y1 - 2010

N2 - Martins et al. (2008) presented what to the best of our knowledge still ranks as the best overall result on the CONLLX 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 obtain 10-20% error reductions, incl. the best reported results on four of them. We compare our approach to other semi supervised learning algorithms.

AB - Martins et al. (2008) presented what to the best of our knowledge still ranks as the best overall result on the CONLLX 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 obtain 10-20% error reductions, incl. the best reported results on four of them. We compare our approach to other semi supervised learning algorithms.

M3 - Article in proceedings

BT - The proceedings of the 23rd International Conference on Computational Linguistics

PB - Association for Computational Linguistics

ER -