Handling Normalization Issues for Part-of-Speech Tagging of Online Conversational Text

International audienceFor the purpose of POS tagging noisy user-generated text, should normalization be handled as a preliminary task or is it possibleto handle misspelled words directly in the POS tagging model? We propose in this paper a combined approach where some errorsare normalized before tagging, while a Gated Recurrent Unit deep neural network based tagger handles the remaining errors. Wordembeddings are trained on a large corpus in order to address both normalization and POS tagging. Experiments are run on ContactCenter chat conversations, a particular type of formal Computer Mediated Communication data