Off-the-shelf part-of-speech taggers typically perform relatively poorly on web and social media texts since those domains are quite different from the newspaper articles on which most tagger models are trained. In this paper, we describe SoMeWeTa, a part-of-speech tagger based on the averaged structured perceptron that is capable of domain adaptation and that can use various external resources. We train the tagger on the German web and social media data of the EmpiriST 2015 shared task. Using the TIGER corpus as background data and adding external information about word classes and Brown clusters, we substantially improve on the state of the art for both the web and the social media data sets. The tagger is available as free software.
@InProceedings{PROISL18.49, author = {Thomas Proisl}, title = "{SoMeWeTa: A Part-of-Speech Tagger for German Social Media and Web Texts}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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