The nature of statistical learning theory
The nature of statistical learning theory
Automatic labeling of semantic roles
Computational Linguistics
Semantic Role Parsing: Adding Semantic Structure to Unstructured Text
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
The necessity of parsing for predicate argument recognition
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A maximum entropy approach to FrameNet tagging
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Target word detection and semantic role chunking using support vector machines
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
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
Use of deep linguistic features for the recognition and labeling of semantic arguments
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Identifying semantic roles using Combinatory Categorial Grammar
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
A lightweight semantic chunking model based on tagging
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Semantic role labeling using different syntactic views
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Learning to predict case markers in Japanese
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
The importance of syntactic parsing and inference in semantic role labeling
Computational Linguistics
A multi-phase approach to biomedical event extraction
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
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
Hybrid learning of dependency structures from heterogeneous linguistic resources
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Parsing syntactic and semantic dependencies with two single-stage maximum entropy models
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
A combined memory-based semantic role labeler of English
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
A puristic approach for joint dependency parsing and semantic role labeling
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
A pipeline approach for syntactic and semantic dependency parsing
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Semantic dependency parsing using n-best semantic role sequences and roleset information
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Dependency tree-based SRL with proper pruning and extensive feature engineering
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Probabilistic model for syntactic and semantic dependency parsing
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Parsing syntactic and semantic dependencies for multiple languages with a pipeline approach
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
A unified knowledge based approach for sense disambiguationm and semantic role labeling
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
The effect of syntactic representation on semantic role labeling
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Adding semantic role annotation to a corpus of written Dutch
LAW '07 Proceedings of the Linguistic Annotation Workshop
Unsupervised argument identification for Semantic Role Labeling
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
Exploration of the LTAG-spinal formalism and Treebank for semantic role labeling
GEAF '09 Proceedings of the 2009 Workshop on Grammar Engineering Across Frameworks
Automatic construction and multi-level visualization of semantic trajectories
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Proceedings of the 2010 conference on Data Mining for Business Applications
Combining constituent and dependency syntactic views for Chinese semantic role labeling
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Developing an algorithm for mining semantics in texts
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
Entropy-Guided feature generation for structured learning of portuguese dependency parsing
PROPOR'12 Proceedings of the 10th international conference on Computational Processing of the Portuguese Language
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In this paper, a novel semantic role labeler based on dependency trees is developed. This is accomplished by formulating the semantic role labeling as a classification problem of dependency relations into one of several semantic roles. A dependency tree is created from a constituency parse of an input sentence. The dependency tree is then linearized into a sequence of dependency relations. A number of features are extracted for each dependency relation using a predefined linguistic context. Finally, the features are input to a set of one-versus-all support vector machine (SVM) classifiers to determine the corresponding semantic role label. We report results on CoNLL2004 shared task data using the representation and scoring scheme adopted for that task.