Application of multilayer perceptron network for tagging parts-of-speech
Ahmed Ahmed, Raju Surampudi Bapi, P.V.S. Chandrasekhar, MHM Krishna Prasad · 2003
This paper presents a neural network based part-of-speech tagger that learns to assign correct part-of-speech tags to the words in a sentence. A multilayer perceptron (MLP) network with three-layers is used. The MLP-tagger is trained with error back-propagation learning algorithm. The representation scheme for the input and output of the network is adapted from Ma et al. (1966). The tagger is trained on SUSANNE English tagged-corpus consisting of 156,622 words. The MLP-tagger is trained using 85% of the corpus. Based on the tag mappings learned, the MLP-tagger demonstrated an accuracy of 90.04% on test data that also included words unseen during the training. Results from our experiments suggest that the MLP-tagger combined with the representation scheme adopted here could be a better substitute for traditional tagging approaches. This method shows promise for addressing parts-of-speech tagging problem for Indian language text considering the fact that most of the Indian language corpora, especially tagged ones, are still considerably small in size.