A Neural Network Model for Part-Of-Speech Tagging of Social Media Texts

Abstract

In this paper, we propose a neural network model for Part-Of-Speech (POS) tagging of User-Generated Content (UGC) such as Twitter, Facebook and Web forums. The proposed model is end-to-end and uses both character and word level representations. Character level representations are learned during the training of the model through a Convolutional Neural Network (CNN). For word level representations, we combine several pre-trainned embeddings (Word2Vec, FastText and GloVe). To deal with the issue of the poor availability of annotated social media data, we have implemented a Transfer Learning (TL) approach. We demonstrate the validity and genericity of our model on a POS tagging task by conducting our experiments on five social media languages (English, German, French, Italian and Spanish).

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