Neural Networks for Part-of-Speech Tagging

The aim of this thesis is to explore the viability of artificial neural networks using a purely contextual word representation as a solution for part-of-speech tagging. Furthermore, the effects of deep learning and increased contextual information of the network are explored. This was achieved by creating an artificial neural network written in Python. The input vectors employed were created by Word2Vec. This system was compared to a baseline using a tagger with handcrafted features in respect to accuracy and precision. The results show that the use of artificial neural networks using a purely contextual word representation shows promise, but ultimately falls roughly two percent short of the baseline. The suspected reason for this is the suboptimal representation for rare words. The use of deeper network architectures shows an insignificant improvement, indicating that the data sets used might be too small. The use of additional context information provided a higher accuracy, but started to decline after a context size of one