A Deep Level Tagger for Malayalam, a Morphologically Rich Language

Author:

Ajees A. P.1,Abrar K. J.2,Sumam Mary Idicula1,Sreenathan M.2

Affiliation:

1. Department of Computer Science, Cochin University of Science and Technology , Cochin , India

2. Department of Linguistics, Thunjath Ezhuthachan Malayalam University , Tirur , India

Abstract

Abstract In recent years, there has been tremendous growth in the amount of natural language text through various sources. Computational analysis of this text has got considerable attention among the NLP researchers. Automatic analysis and representation of natural language text is a step by step procedure. Deep level tagging is one of such steps applied over the text. In this paper, we demonstrate a methodology for deep level tagging of Malayalam text. Deep level tagging is the process of assigning deeper level information to every noun and verb in the text along with normal POS tags. In this study, we move towards a direction that is not much explored in the case of Malayalam language. Malayalam is a morphologically rich and agglutinative language. The morphological features of the language are effectively utilized for the computational analysis of Malayalam text. The language level details required for the study are provided by Thunjath Ezhuthachan Malayalam University, Tirur.

Publisher

Walter de Gruyter GmbH

Subject

Artificial Intelligence,Information Systems,Software

Reference22 articles.

1. AP Ajees and Sumam Mary Idicula, A POS Tagger for Malayalam using Conditional Random Fields.

2. A.R.Rajarajavarma, Kerala panineeyam, (2000).

3. Pedregosa Fabian, Varoquaux Gaël, Gramfort Alexandre, Michel Vincent, Thirion Bertrand, Grisel Olivier, Blondel Mathieu, Prettenhofer Peter, Dubourg Vincent, Vanderplas Jake et al., Scikit-learn: Machine learning in Python, Journal of Machine Learning Research 12 (2011), 2825–2830.

4. Jisha P Jayan, RR Rajeev, S Rajendran et al., Morphological analyser and morphological generator for malayalam-tamil machine translation, International Journal of Computer Applications 13 (2011), 0975–8887.

5. Jurafsky and Martin, Speech and language processsing, (2002).

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