Automatic labeling of semantic roles
Computational Linguistics
Class-Based Construction of a Verb Lexicon
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Learning class-to-class selectional preferences
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Combination strategies for semantic role labeling
Journal of Artificial Intelligence Research
Semantic role labeling: an introduction to the special issue
Computational Linguistics
A Tree Kernel-Based Shallow Semantic Parser for Thematic Role Extraction
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
A preliminary study on the robustness and generalization of role sets for semantic role labeling
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
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We present a sequential Semantic Role Labeling system that describes the tagging problem as a Maximum Entropy Markov Model. The system uses full syntactic information to select BIO-tokens from input data, and classifies them sequentially using state-of-the-art features, with the addition of Selectional Preference features. The system presented achieves competitive performance in the CoNLL-2005 shared task dataset and it ranks first in the SRL subtask of the Semeval-2007 task 17.