Making large-scale support vector machine learning practical
Advances in kernel methods
Automatic labeling of semantic roles
Computational Linguistics
An alternative conception of tree-adjoining derivation
Computational Linguistics
The necessity of parsing for predicate argument recognition
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
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
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Semantic role labeling using different syntactic views
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Semantic role labeling using dependency trees
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Statistical ltag parsing
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
LTAG dependency parsing with bidirectional incremental construction
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
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LTAG-spinal is a novel variant of traditional Lexicalized Tree Adjoining Grammar (LTAG) introduced by (Shen, 2006). The LTAG-spinal Treebank (Shen et al., 2008) combines elementary trees extracted from the Penn Treebank with Propbank annotation. In this paper, we present a semantic role labeling (SRL) system based on this new resource and provide an experimental comparison with CCGBank and a state-of-the-art SRL system based on Treebank phrase-structure trees. Deep linguistic information such as predicate-argument relationships that are either implicit or absent from the original Penn Treebank are made explicit and accessible in the LTAG-spinal Treebank, which we show to be a useful resource for semantic role labeling.