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).
@InProceedings{MEFTAH18.913, author = {Sara Meftah and Nasredine Semmar}, title = "{A Neural Network Model for Part-Of-Speech Tagging of Social Media Texts}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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