Application of Multilayer Perceptron Network for Tagging Parts-of-Speech.
ABSTRACT 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.
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ABSTRACT: In this paper, we describe our recent advances on a novel approach to Part-Of-Speech tagging based on neural networks. Multilayer perceptrons are used following corpus-based learning from contextual, lexical and morphological information. The Penn Treebank corpus has been used for the training and evaluation of the tagging system. The results show that the connectionist approach is feasible and comparable with other approaches.Current Topics in Artificial Intelligence, 13th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2009, Seville, Spain, November 9-13, 2009. Selected Papers; 01/2009
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ABSTRACT: This paper presents the implementation of soft computing (SC) techniques in the field of natural language processing. An attempt is made to design and implement an automatic tagger that extract a free text and then tag it. The part of speech taggers (POS) is the process of categorization words based on their meaning, functions and types (noun, verb, adjective, etc). Two stages tagging system based MPL, FRNN and SVM are implemented and designed. The system helps to classify words and assign the correct POS for each of them. The taggers are tested using two different languages (Arabic and Hindi). The Word disambiguation issue has been solved successfully for Arabic text. Experience has shown that the proposed taggers achieved a great accuracy (99%).International Journal of Computer Applications. 09/2013; 77(8):43-49.
Conference Paper: Arabic part-of-speech tagger based Support Vectors Machines[Show abstract] [Hide abstract]
ABSTRACT: Support vector machines (SVMs) and related kernel methods have become widely known tools for text mining tasks such as classification and regression. The Arabic part of speech (POS) based support vectors machine is designed and implemented. The NeuroSolutions software is used to adopt and learn the proposed tagger. The radial basis functions (RBFs) is used as a linear function approximator. The experiments has give an evinced that the SVMs tagger is accurate of (99.99%), has low processing time, and use a little a mount of data at training phase.Information Technology, 2008. ITSim 2008. International Symposium on; 09/2008