Making large-scale support vector machine learning practical
Advances in kernel methods
Automatic labeling of semantic roles
Computational Linguistics
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Support Vector Learning for Semantic Argument Classification
Machine Learning
Using LTAG based features in parse reranking
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Semantic role labeling using different syntactic views
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Joint learning improves semantic role labeling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
The necessity of syntactic parsing for semantic role labeling
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Hierarchical semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
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
Efficient linearization of tree kernel functions
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Coreference systems based on kernels methods
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Re-ranking models for spoken language understanding
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Syntactic and semantic kernels for short text pair categorization
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Syntactic Structural Kernels for Natural Language Interfaces to Databases
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
FBK-IRST: kernel methods for semantic relation extraction
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
RTV: tree kernels for thematic role classification
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Encoding tree pair-based graphs in learning algorithms: the textual entailment recognition case
TextGraphs-3 Proceedings of the 3rd Textgraphs Workshop on Graph-Based Algorithms for Natural Language Processing
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Re-ranking models based-on small training data for spoken language understanding
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Rule extraction from support vector machines: A review
Neurocomputing
Kernel engineering for fast and easy design of natural language applications
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Kernel Engineering for Fast and Easy Design of Natural Language Applications
Unified Semantic Role Labeling for Verbal and Nominal Predicates in the Chinese Language
ACM Transactions on Asian Language Information Processing (TALIP)
Semantic mapping between natural language questions and SQL queries via syntactic pairing
NLDB'09 Proceedings of the 14th international conference on Applications of Natural Language to Information Systems
Inferring the semantic properties of sentences by mining syntactic parse trees
Data & Knowledge Engineering
Exploiting syntactic and semantic relationships between terms for opinion retrieval
Journal of the American Society for Information Science and Technology
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Recent work on Semantic Role Labeling (SRL) has shown that to achieve high accuracy a joint inference on the whole predicate argument structure should be applied. In this paper, we used syntactic subtrees that span potential argument structures of the target predicate in tree kernel functions. This allows Support Vector Machines to discern between correct and incorrect predicate structures and to re-rank them based on the joint probability of their arguments. Experiments on the PropBank data show that both classification and re-ranking based on tree kernels can improve SRL systems.