Semantic Word Spaces for Robust Role Labeling. Giannone, C., Croce, D., & Basili, R. In 2009 International Conference on Machine Learning and Applications, pages 261-266, 12, 2009. IEEE.
Website abstract bibtex Semantic role labeling systems are often designed as inductive processes over annotated resources. Supervised algorithms based on complex grammatical information achieve state-of-the-art accuracy. However, their generalization on the argument classification task is poorer, as large performance drops in out-of-domain tests showed. In this paper, a robust method based on a minimal set of grammatical features and a distributional model of lexical semantic information is proposed. The achievable generalization ability is studied in several training conditions where negligible performance drops are observed.
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abstract = {Semantic role labeling systems are often designed as inductive processes over annotated resources. Supervised algorithms based on complex grammatical information achieve state-of-the-art accuracy. However, their generalization on the argument classification task is poorer, as large performance drops in out-of-domain tests showed. In this paper, a robust method based on a minimal set of grammatical features and a distributional model of lexical semantic information is proposed. The achievable generalization ability is studied in several training conditions where negligible performance drops are observed.},
bibtype = {inProceedings},
author = {Giannone, Cristina and Croce, Danilo and Basili, Roberto},
booktitle = {2009 International Conference on Machine Learning and Applications}
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