Non-lexical neural architecture for fine-grained POS Tagging
Matthieu Labeau, Kevin Löser, Alexandre Allauzen · 2015
In this paper we explore a POS tagging application of neural architectures that can infer word representations from the raw character stream.It relies on two modelling stages that are jointly learnt: a convolutional network that infers a word representation directly from the character stream, followed by a prediction stage.Models are evaluated on a POS and morphological tagging task for German.Experimental results show that the convolutional network can infer meaningful word representations, while for the prediction stage, a well designed and structured strategy allows the model to outperform stateof-the-art results, without any feature engineering.