Higher-order dependency features are known to improve dependency parser accuracy. We investigate the incorporation of such features into a cube decoding phrase-structure parser. We find considerable gains in accuracy on the range of standard metrics. What is especially interesting is that we find strong, statistically significant gains on dependency recovery on out-of-domain tests (Brown vs. WSJ). This suggests that higher-order dependency features are not simply overfitting the training material.
REF incorporated higher-order dependency features into a cube decoding phrasestructure parser and obtained significant gains on dependency recovery for both in-domain and out-of-domain test sets.
The Effect of Higher-Order Dependency Features in Discriminative Phrase-Structure Parsing
ACL
Computer Science