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To address the challenges of normalizing online conversational texts prevalent in social media, we propose a contextual long-short term memory (LSTM) recurrent neural network based approach, augmented with a self-generated dictionary normalization technique. Our approach utilizes a sequence of characters as well as the part-of-speech associated with words without harnessing any external lexical resources. This work is evaluated on the English Tweet data set provided by the ACL 2015 W-NUT Normalization of Noisy Text shared task. The results, by achieving second place (F1 score: 81.75%) in the constrained track of the competition, indicate that the proposed LSTM-based approach is a promising tool for normalizing non-standard language.

NCSU_SAS_WOOKHEE: A Deep Contextual Long-Short Term Memory Model for Text Normalization