Paper: Probabilistic Model for Syntactic and Semantic Dependency Parsing

ACL ID W08-2139
Title Probabilistic Model for Syntactic and Semantic Dependency Parsing
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2008
Authors

This paper proposes a novel method to analyze syntactic dependencies and label semantic dependencies around both the verbal predicates and the nouns. In this method, a probabilistic model is designed to obtain a global optimal result. More- over, a predicate identification model and a disambiguation model are proposed to label predicates and their senses. The ex- perimental results obtained on the wsj and brown test sets show that our system obtains 77% of labeled macro F1 score for the whole task, 84.47% of labeled at- tachment score for syntactic dependency task, and 69.45% of labeled F1 score for semantic dependency task.