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An Improved Stemming Approach Using HMM for a Highly Inflectional Language

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7816)

Abstract

Stemming is a common method for morphological normalization of natural language texts. Modern information retrieval systems rely on such normalization techniques for automatic document processing tasks. High quality stemming is difficult in highly inflectional Indic languages. Little research has been performed on designing algorithms for stemming of texts in Indic languages. In this study, we focus on the problem of stemming texts in Assamese, a low resource Indic language spoken in the North-Eastern part of India by approximately 30 million people. Stemming is hard in Assamese due to the common appearance of single letter suffixes as morphological inflections. More than 50% of the inflections in Assamese appear as single letter suffixes. Such single letter morphological inflections cause ambiguity when predicting underlying root word. Therefore, we propose a new method that combines a rule based algorithm for predicting multiple letter suffixes and an HMM based algorithm for predicting the single letter suffixes. The combined approach can predict morphologically inflected words with 92% accuracy.

Keywords

Indic Language Root Word South Asian Language Southeast Asian Natural SIGIR Forum 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of CSETezpur UniversityIndia
  2. 2.Department of MIUniversity of British ColumbiaCanada
  3. 3.Department of CSUniversity of Colorado at Colorado SpringsUSA

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