An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Automatic labeling of semantic roles
Computational Linguistics
The disambiguation of nominalizations
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
Immediate-head parsing for language models
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
The necessity of parsing for predicate argument recognition
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
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
Automatic labeling of semantic roles
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Use of support vector learning for chunk identification
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Semantic interpretation of nominalizations
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Semantic role labeling of nominalized predicates in Chinese
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Automatically extracting nominal mentions of events with a bootstrapped probabilistic classifier
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Labeling chinese predicates with semantic roles
Computational Linguistics
A pipeline approach for syntactic and semantic dependency parsing
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Semantic role assignment for event nominalisations by leveraging verbal data
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Semantic Role Labeling of NomBank: a maximum entropy approach
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Semantic labeling of compound nominalization in Chinese
MWE '07 Proceedings of the Workshop on a Broader Perspective on Multiword Expressions
Mining of parsed data to derive deverbal argument structure
GEAF '09 Proceedings of the 2009 Workshop on Grammar Engineering Across Frameworks
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Identification of Chinese verb nominalization using support vector machine
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Arguments of nominals in semantic interpretation of biomedical text
BioNLP '10 Proceedings of the 2010 Workshop on Biomedical Natural Language Processing
Unified Semantic Role Labeling for Verbal and Nominal Predicates in the Chinese Language
ACM Transactions on Asian Language Information Processing (TALIP)
Empirical methods for the study of denotation in nominalizations in spanish
Computational Linguistics
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In this paper, we use a machine learning framework for semantic argument parsing, and apply it to the task of parsing arguments of eventive nominalizations in the FrameNet database. We create a baseline system using a subset of features introduced by Gildea and Jurafsky (2002), which are directly applicable to nominal predicates. We then investigate new features which are designed to capture the novelties in nominal argument structure and show a significant performance improvement using these new features. We also investigate the parsing performance of nominalizations in Chinese and compare the salience of the features for the two languages.