@inProceedings{
 title = {Semantic Word Spaces for Robust Role Labeling},
 type = {inProceedings},
 year = {2009},
 identifiers = {[object Object]},
 keywords = {distributional models,low semantic parsing,machine learning,shal},
 pages = {261-266},
 websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5381852},
 month = {12},
 publisher = {IEEE},
 id = {0da8662e-0476-350b-8c6c-957a8ca431f5},
 created = {2012-04-01T16:32:49.000Z},
 accessed = {2012-03-28},
 file_attached = {false},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 tags = {semantic role labeling},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Giannone2009a},
 private_publication = {false},
 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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