Automatic labeling of semantic roles
Computational Linguistics
The Automatic Interpretation of Nominalizations
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
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
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
SemEval-2007 task 04: classification of semantic relations between nominals
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Beyond NomBank: a study of implicit arguments for nominal predicates
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Unified Semantic Role Labeling for Verbal and Nominal Predicates in the Chinese Language
ACM Transactions on Asian Language Information Processing (TALIP)
Automatic crime prediction using events extracted from twitter posts
SBP'12 Proceedings of the 5th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Automatic Identification and Classification of Noun Argument Structures in Biomedical Literature
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Semantic role labeling of implicit arguments for nominal predicates
Computational Linguistics
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Nominals frequently surface without overtly expressed arguments. In order to measure the potential benefit of nominal SRL for downstream processes, such nominals must be accounted for. In this paper, we show that a state-of-the-art nominal SRL system with an overall argument F1 of 0.76 suffers a performance loss of more than 9% when nominals with implicit arguments are included in the evaluation. We then develop a system that takes implicit argumentation into account, improving overall performance by nearly 5%. Our results indicate that the degree of implicit argumentation varies widely across nominals, making automated detection of implicit argumentation an important step for nominal SRL.