Publication Type:

Journal Article


Communications in Computer and Information Science, Volume 101, Kochi, Kerala, p.430-433 (2010)





Grammatical structure, Information technology, Learning algorithms, Learning systems, Machine learning approaches, Morphemes, Morphological analyzer, Morphological features, NAtural language processing, Natural language processing systems, Non-linear relationships, Rule-based approach, Support vector machines


In this paper, we presented a morphological analyzer for the classical Dravidian language Telugu using machine learning approach. Morphological analyzer is a computer program that analyses the words belonging to Natural Languages and produces its grammatical structure as output. Telugu language is highly inflection and suffixation oriented, therefore developing the morphological analyzer for Telugu is a significant task. The developed morphological analyzer is based on sequence labeling and training by kernel methods, it captures the non-linear relationships and various morphological features of Telugu language in a better and simpler way. This approach is more efficient than other morphological analyzers which were based on rules. In rule based approach every rule is depends on the previous rule. So if one rule fails, it will affect the entire rule that follows. Regarding the accuracy our system significantly achieves a very competitive accuracy of 94% and 97% in case of Telugu Verbs and nouns. Morphological analyzer for Tamil and Malayalam was also developed by using this approach. © Springer-Verlag Berlin Heidelberg 2010.


cited By 0; Conference of International Conference on Information and Communication Technologies, ICT 2010 ; Conference Date: 7 September 2010 Through 9 September 2010; Conference Code:98068

Cite this Research Publication

S. Kiranmai, M Kumar, A., Dhanalakshmi, Vb, Soman, K. Pb, V.V, D., R., V., V., S., K.G., S., H.A., A., M., H., D.K., T., A., M., B., J., E., A., and J., B., “Morphological Analyzer for Telugu Using Support Vector Machine”, Communications in Computer and Information Science, vol. 101, pp. 430-433, 2010.