{"_id":"gkgEpYwSLNejHLwJp","bibbaseid":"ponzetto-strube-semanticrolelabelingusinglexicalstatisticalinformation-2005","authorIDs":[],"author_short":["Ponzetto, S., P.","Strube, M."],"bibdata":{"title":"Semantic Role Labeling Using Lexical Statistical Information","type":"inProceedings","year":"2005","pages":"213-216","issue":"June","websites":"http://www.aclweb.org/anthology/W/W05/W05-0633","publisher":"Association for Computational Linguistics","id":"62d93b11-ec50-3e42-98b8-939250c1d7f9","created":"2012-02-28T00:51:15.000Z","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","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"Ponzetto2005","private_publication":false,"abstract":"Our system for semantic role labeling is multi-stage in nature, being based on tree pruning techniques, statistical methods for lexicalised feature encoding, and a C4.5 decision tree classifier. We use both shal- low and deep syntactic information from automatically generated chunks and parse trees, and develop a model for learning the semantic arguments of predicates as a multi-class decision problem. We evalu- ate the performance on a set of relatively ‘cheap’ features and report an F1 score of 68.13% on the overall test set.","bibtype":"inProceedings","author":"Ponzetto, Simone Paolo and Strube, Michael","booktitle":"Proceedings of the Ninth Conference on Computational Natural Language Learning CoNLL2005","bibtex":"@inProceedings{\n title = {Semantic Role Labeling Using Lexical Statistical Information},\n type = {inProceedings},\n year = {2005},\n pages = {213-216},\n issue = {June},\n websites = {http://www.aclweb.org/anthology/W/W05/W05-0633},\n publisher = {Association for Computational Linguistics},\n id = {62d93b11-ec50-3e42-98b8-939250c1d7f9},\n created = {2012-02-28T00:51:15.000Z},\n file_attached = {false},\n profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},\n group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},\n last_modified = {2017-03-14T14:36:19.698Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Ponzetto2005},\n private_publication = {false},\n abstract = {Our system for semantic role labeling is multi-stage in nature, being based on tree pruning techniques, statistical methods for lexicalised feature encoding, and a C4.5 decision tree classifier. We use both shal- low and deep syntactic information from automatically generated chunks and parse trees, and develop a model for learning the semantic arguments of predicates as a multi-class decision problem. We evalu- ate the performance on a set of relatively ‘cheap’ features and report an F1 score of 68.13% on the overall test set.},\n bibtype = {inProceedings},\n author = {Ponzetto, Simone Paolo and Strube, Michael},\n booktitle = {Proceedings of the Ninth Conference on Computational Natural Language Learning CoNLL2005}\n}","author_short":["Ponzetto, S., P.","Strube, M."],"urls":{"Website":"http://www.aclweb.org/anthology/W/W05/W05-0633"},"bibbaseid":"ponzetto-strube-semanticrolelabelingusinglexicalstatisticalinformation-2005","role":"author","downloads":0,"html":""},"bibtype":"inProceedings","creationDate":"2020-02-06T23:48:12.036Z","downloads":0,"keywords":[],"search_terms":["semantic","role","labeling","using","lexical","statistical","information","ponzetto","strube"],"title":"Semantic Role Labeling Using Lexical Statistical Information","year":2005}