Application of Multilayer Perceptron Network for Tagging Parts-of-Speech.

Conference Paper · January 2002with48 Reads
DOI: 10.1109/LEC.2002.1182291 · Source: DBLP
Conference: 2002 Language Engineering Conference (LEC 2002), 13-15 December 2002, Hyderabad, India
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.
    • "Section 3 introduces the Tamil language and the general morphophonemic concepts related to this topic. Description of the machine learning techniques used in this paper is given in Section 4. The implementation and the results of the machine learning models are discussed in Section 5. [2] RELATED WORKS "
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