For the purpose of POS tagging noisy user-generated text, should normalization be handled as a preliminary task or is it possible to handle misspelled words directly in the POS tagging model? We propose in this paper a combined approach where some errors are normalized before tagging, while a Gated Recurrent Unit deep neural network based tagger handles the remaining errors. Word embeddings are trained on a large corpus in order to address both normalization and POS tagging. Experiments are run on Contact Center chat conversations, a particular type of formal Computer Mediated Communication data.
@InProceedings{DAMNATI18.357, author = {Géraldine Damnati and Jérémy Auguste and Alexis Nasr and Delphine Charlet and Johannes Heinecke and Frédéric Béchet}, title = "{Handling Normalization Issues for Part-of-Speech Tagging of Online Conversational Text}", 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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