Exploring multilingual semantic role labeling. Li, B., Emms, M., Luz, S., & Vogel, C. In Proceedings of the Thirteenth Conference on Computational Natural Language Learning Shared Task CoNLL 09, pages 73-78, 2009. Association for Computational Linguistics.
Exploring multilingual semantic role labeling [link]Website  abstract   bibtex   
This paper describes the multilingual semantic role labeling system of Computational Linguistics Group, Trinity College Dublin, for the CoNLL-2009 SRLonly closed shared task. The system consists of two cascaded components: one for disambiguating predicate word sense, and the other for identifying and classifying arguments. Supervised learning techniques are utilized in these two components. As each language has its unique characteristics, different parameters and strategies have to be taken for different languages, either for providing functions required by a language or for meeting the tight deadline. The system obtained labeled F1 69.26 averaging over seven languages (Catalan, Chinese, Czech, English, German, Japanese, and Spanish), which ranks the system fourth among the seven systems participating the SRL only closed track.
@inProceedings{
 title = {Exploring multilingual semantic role labeling},
 type = {inProceedings},
 year = {2009},
 identifiers = {[object Object]},
 pages = {73-78},
 issue = {June},
 websites = {http://portal.acm.org/citation.cfm?doid=1596409.1596422},
 publisher = {Association for Computational Linguistics},
 id = {c7782bb9-2699-381a-a11a-0a45be148b34},
 created = {2012-04-01T16:32:49.000Z},
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 last_modified = {2017-03-14T14:36:19.698Z},
 tags = {semantic role labeling},
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 abstract = {This paper describes the multilingual semantic role labeling system of Computational Linguistics Group, Trinity College Dublin, for the CoNLL-2009 SRLonly closed shared task. The system consists of two cascaded components: one for disambiguating predicate word sense, and the other for identifying and classifying arguments. Supervised learning techniques are utilized in these two components. As each language has its unique characteristics, different parameters and strategies have to be taken for different languages, either for providing functions required by a language or for meeting the tight deadline. The system obtained labeled F1 69.26 averaging over seven languages (Catalan, Chinese, Czech, English, German, Japanese, and Spanish), which ranks the system fourth among the seven systems participating the SRL only closed track.},
 bibtype = {inProceedings},
 author = {Li, Baoli and Emms, Martin and Luz, Saturnino and Vogel, Carl},
 booktitle = {Proceedings of the Thirteenth Conference on Computational Natural Language Learning Shared Task CoNLL 09}
}

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