Paper: Deterministic Dependency Parsing Of English Text

ACL ID C04-1010
Title Deterministic Dependency Parsing Of English Text
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2004
Authors

This paper presents a deterministic dependency parser based on memory-based learning, which parses English text in linear time. When trained and evaluated on the Wall Street Journal sec- tion of the Penn Treebank, the parser achieves a maximum attachment score of 87.1%. Unlike most previous systems, the parser produces la- beled dependency graphs, using as arc labels a combination of bracket labels and grammatical role labels taken from the Penn Treebank II an- notation scheme. The best overall accuracy ob- tained for identifying both the correct head and the correct arc label is 86.0%, when restricted to grammatical role labels (7 labels), and 84.4% for the maximum set (50 labels).