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Automatic Domain Adaptation for Parsing

David McClovsky, Eugene Charniak, Mark Johnson (ACL 2010)

Presented by: Nathan Green
Report by: Katerina Topilova



Idea – when parsing large data from diverse domains, it is useful for parsers to be able to generalize to a variety of domains.
The result is a system that proposes linear combinations of parsing models trained on the source corpora.

Evaluation – 2 scenarios – out-of-domain evaluation, in-domain evaluation
Baselines – Uniform, Self-Trained Uniform, Fixed Set: WSJ, Best Single Corpus, Best Seen, Best Overall
Feature selection – round-robin tuning scenario


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