Comparing two trainable grammatical relations finders
<|reference_start|>Comparing two trainable grammatical relations finders: 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.<|reference_end|>
@article{yeh2000comparing,
    title={Comparing two trainable grammatical relations finders},
    author={Alexander Yeh},
    journal={18th International Conference on Computational Linguistics (COLING
  2000), pages 1146-1150, Saarbruecken, Germany, July, 2000},
    year={2000},
    archivePrefix={arXiv},
    eprint={cs/0008004},
    primaryClass={cs.CL}
}