A Neural Network Model for Part-Of-Speech Tagging of Social Media Texts
Sara Meftah, Nasredine Semmar · 2018
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).