Document clustering with committees
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic labeling of semantic roles
Computational Linguistics
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
COGEX: a logic prover for question answering
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Hierarchical semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Evolutionary hypernetwork classifiers for protein-proteininteraction sentence filtering
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
The effect of syntactic representation on semantic role labeling
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Dependency-based semantic role labeling of PropBank
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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The integration of sophisticated inference-based techniques into natural language processing applications first requires a reliable method of encoding the predicate-argument structure of the propositional content of text. Recent statistical approaches to automated predicate-argument annotation have utilized parse tree paths as predictive features, which encode the path between a verb predicate and a node in the parse tree that governs its argument. In this paper, we explore a number of alternatives for how these parse tree paths are encoded, focusing on the difference between automatically generated constituency parses and dependency parses. After describing five alternatives for encoding parse tree paths, we investigate how well each can be aligned with the argument substrings in annotated text corpora, their relative precision and recall performance, and their comparative learning curves. Results indicate that constituency parsers produce parse tree paths that can more easily be aligned to argument substrings, perform better in precision and recall, and have more favorable learning curves than those produced by a dependency parser.