Comparing two trainable grammatical relations finders

Authors:
Alexander Yeh
Affiliations:
Mitre Corp., Bedford, MA
Venue:
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Year:
2000

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Abstract

Grammatical relationships (GRs) form an important level of natural language processing, but different sets of GRs are useful for different purposes. Therefore, one may often only have time to obtain a small training corpus with the desired GR annotations. On such a small training corpus, we compare two systems. They use different learning techniques, but we find that this difference by itself only has a minor effect. A larger factor is that in English, a different GR length measure appears better suited for finding simple argument GRs than for finding modifier GRs. We also find that partitioning the data may help memory-based learning.