Bootstrapping Lexicalized Models in Memory-Based Dependency Parsing

Previous research has shown that a lexicalized parsing model incorporating words but no parts-of-speech can outperform a model involving partsof-speech but no words given enough training data for supervised learning. We show that the same effect can be achieved with a bootstrapping approach, where a mixed model trained on a small treebank is used to parse a larger corpus which is used as training data for the lexicalized model. The results are obtained using a memory-based learning algorithm for deterministic dependency parsing of unrestricted Swedish text.