We present a practical co-training method for bootstrapping statistical parsers using a small amount of manually parsed training material and a much larger pool of raw sentences. Experimental results show that unlabelled sentences can be used to improve the performance of statistical parsers. In addition, we consider the problem of bootstrapping parsers when the manually parsed training material is in a different domain to either the raw sentences or the testing material. We show that bootstrapping continues to be useful, even though no manually produced parses from the target domain are used.
|Title of host publication||Proceedings of the Tenth Conference on European Chapter of the Association for Computational Linguistics - Volume 1|
|Place of Publication||Stroudsburg, PA, USA|
|Publisher||Association for Computational Linguistics|
|Number of pages||8|
|Publication status||Published - 2003|