A maximum entropy approach to natural language processing
Computational Linguistics
Automatic labeling of semantic roles
Computational Linguistics
Semantic role labeling using dependency trees
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
The CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Semantic role lableing system using maximum entropy classifier
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
The CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Multilingual joint parsing of syntactic and semantic dependencies with a latent variable model
Computational Linguistics
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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. Moreover, a predicate identification model and a disambiguation model are proposed to label predicates and their senses. The experimental results obtained on the waj and brown test sets show that our system obtains 77% of labeled macro F1 score for the whole task, 84.47% of labeled attachment score for syntactic dependency task, and 69.45% of labeled F1 score for semantic dependency task.