Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Automatic labeling of semantic roles
Computational Linguistics
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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
A global joint model for semantic role labeling
Computational Linguistics
Labeling chinese predicates with semantic roles
Computational Linguistics
The importance of syntactic parsing and inference in semantic role labeling
Computational Linguistics
SemEval-2007 task 09: multilevel semantic annotation of Catalan and Spanish
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
ILK2: semantic role labelling for Catalan and Spanish using TiMBL
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Combination strategies for semantic role labeling
Journal of Artificial Intelligence Research
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Semantic role labeling as sequential tagging
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Semantic role chunking combining complementary syntactic views
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
| Hi-index | 0.00 |
This paper analyzes two joint inference approaches for semantic role labeling: re-ranking of candidate semantic frames generated by one local model and combination of two distinct models at argument-level using meta learning. We perform an empirical analysis on two recently released corpora of annotated semantic roles in Spanish and Catalan. This work yields several novel conclusions: (a) the proposed joint inference strategies yield good results even under adverse conditions: small training corpora, only two individual models available for combination, minimal output available from the individual models; (b) stacking of the two joint inference approaches is successful, which indicates that the two inference models provide complementary benefits. Our results are currently the best for the identification of semantic role for Spanish and Catalan.