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Publication Type : Conference Paper
Publisher : Procedia Computer Science
Source : Procedia Computer Science, Elsevier B.V., Volume 132, p.47-54 (2018)
Keywords : Computational processing, Deep learning, Long short-term memory, LSTM, Malayalam Morphological Analysis, Malayalams, Morphological analysis, Morphological information, Morphology, Recurrent neural network (RNN), Sandhi splitting, Syntactics
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Computer Science, Electronics and Communication
Year : 2018
Abstract : Morphological analysis is one of the fundamental tasks in computational processing of natural languages. It is the study of the rules of word construction by analysing the syntactic properties and morphological information. In order to perform this task, morphemes have to be separated from the original word. This process is termed as sandhi splitting. Sandhi splitting is important in the morphological analysis of agglutinative languages like Malayalam, because of the richness in morphology, inflections and sandhi. Due to sandhi, many morphological changes occur at the conjoining position of morphemes. Therefore, determining the morpheme boundaries becomes a tough task, especially in languages like Malayalam. In this paper, we propose a deep learning approach for learning the rules for identifying the morphemes automatically and segmenting them from the original word. Then, individual morphemes can be further analysed to identify the grammatical structure of the word. Three different systems were developed for this analysis using Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) and obtained accuracies 98.08%, 97.88% and 98.16% respectively. © 2018 The Authors. Published by Elsevier Ltd.
Cite this Research Publication : B. Premjith, Dr. Soman K. P., and Anand Kumar M., “A deep learning approach for Malayalam morphological analysis at character level”, in Procedia Computer Science, 2018, vol. 132, pp. 47-54.