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dc.contributor.authorStemle E
dc.date.accessioned2019-03-01T14:34:42Z
dc.date.available2019-03-01T14:34:42Z
dc.date.issued2016
dc.identifier.urihttp://dx.doi.org/10.18653/v1/W16-2614
dc.identifier.urihttp://aclweb.org/anthology/W16-2614
dc.identifier.urihttp://hdl.handle.net/10863/8909
dc.description.abstractThis art­icle describes the sys­tem that par­ti­cip­ated in the Part-of-speech tag­ging sub­task of the "Em­pir­iST 2015 shared task on auto­matic lin­guistic annota­tion of com­puter­-­me­di­ated com­mu­nic­a­tion / social medi­a". The sys­tem com­bines a small asser­tion of trend­ing tech­niques, which imple­ment matured meth­ods, from NLP and ML to achieve com­pet­it­ive res­ults on PoS tag­ging of Ger­man CMC and Web cor­pus data; in par­tic­u­lar, the sys­tem uses word embed­dings and char­ac­ter­-­level rep­res­ent­a­tions of word begin­nings and end­ings in a LSTM RNN archi­tec­ture. Labelled data (Ti­ger v2.2 and Empir­iST) and unla­belled data (Ger­man Wiki­pe­dia) were used for train­ing. The sys­tem is avail­able under the APLv2 open-­source license.en_US
dc.languageEnglish
dc.language.isoenen_US
dc.relation10th Web as Corpus Workshop (WAC-X) ; Berlin : 12.8.2016 - 12.8.2016
dc.rights
dc.titlebot.zen @ EmpiriST 2015: A minimally-deep learning PoS-tagger (trained for German CMC and Web data)en_US
dc.typeBook chapteren_US
dc.date.updated2019-03-01T11:47:33Z
dc.publication.title
dc.language.isiEN-GB
dc.description.fulltextnoneen_US


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