A Maximum Entropy Model for Part-of-Speech Tagging Export

@incollection{citeulike:346224, abstract = {This paper presents a statistical model which trains from a corpus annotated with Part-OfSpeech tags and assigns them to previously unseen text with state-of-the-art accuracy(96.6\%). The model can be classified as a Maximum Entropy model and simultaneously uses many contextual \"features\" to predict the POS tag. Furthermore, this paper demonstrates the use of specialized features to model difficult tagging decisions, discusses the corpus consistency problems discovered during the implementation...}, address = {Somerset, New Jersey}, author = {Ratnaparkhi, Adwait}, booktitle = {Proceedings of the Conference on Empirical Methods in Natural Language Processing}, citeulike-article-id = {346224}, citeulike-linkout-0 = {http://citeseer.ist.psu.edu/ratnaparkhi96maximum.html}, citeulike-linkout-1 = {http://citeseer.lcs.mit.edu/ratnaparkhi96maximum.html}, citeulike-linkout-2 = {http://citeseer.ifi.unizh.ch/ratnaparkhi96maximum.html}, citeulike-linkout-3 = {http://citeseer.comp.nus.edu.sg/ratnaparkhi96maximum.html}, editor = {Brill, Eric and Church, Kenneth}, keywords = {maxent, pos-tagging}, pages = {133--142}, posted-at = {2007-04-03 22:45:10}, priority = {2}, publisher = {Association for Computational Linguistics}, title = {A Maximum Entropy Model for Part-of-Speech Tagging}, url = {http://citeseer.ist.psu.edu/ratnaparkhi96maximum.html}, year = {1996} }

TY - CHAP ID - citeulike:346224 L3 - citeulike-article-id:346224 N2 - This paper presents a statistical model which trains from a corpus annotated with Part-OfSpeech tags and assigns them to previously unseen text with state-of-the-art accuracy(96.6%). The model can be classified as a Maximum Entropy model and simultaneously uses many contextual "features" to predict the POS tag. Furthermore, this paper demonstrates the use of specialized features to model difficult tagging decisions, discusses the corpus consistency problems discovered during the implementation... AD - Somerset, New Jersey EP - 142 TI - A Maximum Entropy Model for Part-of-Speech Tagging PB - Association for Computational Linguistics T2 - Proceedings of the Conference on Empirical Methods in Natural Language Processing SP - 133 KW - maxent KW - pos-tagging AU - Ratnaparkhi, Adwait A2 - Brill, Eric A2 - Church, Kenneth PY - 1996/// UR - http://citeseer.ist.psu.edu/ratnaparkhi96maximum.html ER -