Part-of-speech (POS) tagging

The aim of this project is to perform POS tagging as sequence labelling using Recurrent Neural Architectures (RNN)
The focus is on the f1-score of four different models

RNN models

• Bidirectional LSTM (Bi-LSTM)
• Bidirectional GRU (Bi-GRU)
• Two Bi-LSTM in sequence
• Bi-LSTM and a Dense layer

The last layer of all models is a dense layer used as classifier