We describe our experiments using the DeSR parser in the multilingual and domain adaptation tracks of the CoNLL 2007 shared task. DeSR implements an incremental deterministic Shift/Reduce parsing algorithm, using specific rules to handle non-projective dependencies. For the multilingual track we adopted a second order averaged perceptron and performed feature selection to tune a feature model for each language. For the domain adaptation track we applied a tree revision method which learns how to correct the mistakes made by the base parser on the adaptation domain.
In the CoNLL 2007 shared task on domain adaptation for dependency parsing, REF used a tree revision method that corrects the mistakes caused by the base parser for the target domain.
Multilingual Dependency Parsing and Domain Adaptation using DeSR
2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)
Computer Science