On building a more efficient grammar by exploiting types
Natural Language Engineering
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Estimators for stochastic "Unification-Based" grammars
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
The LinGO Redwoods treebank motivation and preliminary applications
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 2
Annotating wall street journal texts using a hand-crafted deep linguistic grammar
ACL-IJCNLP '09 Proceedings of the Third Linguistic Annotation Workshop
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This paper presents a novel approach of incorporating fine-grained treebanking decisions made by human annotators as discriminative features for automatic parse disambiguation. To our best knowledge, this is the first work that exploits treebanking decisions for this task. The advantage of this approach is that use of human judgements is made. The paper presents comparative analyses of the performance of discriminative models built using treebanking decisions and state-of-the-art features. We also highlight how differently these features scale when these models are tested on out-of-domain data. We show that, features extracted using treebanking decisions are more efficient, informative and robust compared to traditional features.