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