Paper: Comparing Two Trainable Grammatical Relations Finders

ACL ID C00-2175
Title Comparing Two Trainable Grammatical Relations Finders
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2000
Authors

Grammatical relationships (Glls) form an im- portant level of natural language processing, but different sets of ORs are useflfl for different purposes. Theretbre, one may often only have time to obtain a small training corpus with the desired GI1. annotations. On su& a small train- ing corpus, we compare two systems. They use difl'erent learning tedmiques, but we find that this difference by itself only has a minor effect. A larger factor is that iLL English, a different GI/. length measure appears better suited for finding simple m:gument GIs than ~br finding modifier GRs. We also find that partitioning the data ma W help memory-based learning.