Stochastic dependency parsers can achieve very good results when they are trained on large corpora that have been manually annotated. Active learning is a procedure that aims at reducing this annotation cost by selecting as few sentences as possible that will produce the best possible parser. We propose a new selective sampling function for Active Learning that exploits two memory-based distances to find a good compromise between parser uncertainty and sentence representativeness. The reduced dependency between both parsing and selection models opens interesting perspectives for future models combination. The approach is validated on a French broadcast news corpus creation task dedicated to dependency parsing. It outperforms the baseline uncertainty entropy-based selective sampling on this task. We plan to extend this work with self- and co-training methods in order to enlarge this corpus and produce the first French broadcast news Tree Bank.
Cite as: Tantini, F., Cerisara, C., Gardent, C. (2010) Memory-based active learning for French broadcast news. Proc. Interspeech 2010, 1377-1380, doi: 10.21437/Interspeech.2010-420
@inproceedings{tantini10_interspeech,
author={Frédéric Tantini and Christophe Cerisara and Claire Gardent},
title={{Memory-based active learning for French broadcast news}},
year=2010,
booktitle={Proc. Interspeech 2010},
pages={1377--1380},
doi={10.21437/Interspeech.2010-420}
}