Automatic labeling of semantic roles
Computational Linguistics
Combining Classifiers for word sense disambiguation
Natural Language Engineering
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Using predicate-argument structures for information extraction
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Support Vector Learning for Semantic Argument Classification
Machine Learning
The framenet model and its applications†
Natural Language Engineering
Automatic Frame Extraction from Sentences
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
A comparative study on generalization of semantic roles in FrameNet
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Using topic themes for multi-document summarization
ACM Transactions on Information Systems (TOIS)
Unsupervised event coreference resolution with rich linguistic features
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Chinese frame identification using T-CRF model
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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This paper describes our system for the task of extracting frame semantic structures in SemEval--2007. The system architecture uses two types of learning models in each part of the task: Support Vector Machines (SVM) and Maximum Entropy (ME). Designed as a pipeline of classifiers, the semantic parsing system obtained competitive precision scores on the test data.