Echo state network for word sense disambiguation

Abstract

The current developments in the area report on numerous applications of recurrent neural networks for Word Sense Disambiguation that allowed the increase of prediction accuracy even in situation with sparse knowledge due to the available generalization properties. Since the traditionally used LSTM networks demand enormous computational power and time to be trained, the aim of the present work is to investigate the possibility of applying a recently proposed fast trainable RNN, namely Echo state networks. The preliminary results reported here demonstrate the applicability of ESN to WSD.