A Rule Based Lightweight Bengali Stemmer

Souvick Das, Rajat Pandit, Sudip Kumar Naskar

Abstract
In the field of Natural Language Processing (NLP) the process of stemming plays a significant role. Stemmer transforms an inflected word to its root form. Stemmer significantly increases the efficiency of Information Retrieval (IR) systems. It is a very basic yet fundamental text pre-processing task widely used in many NLP tasks. Several important works on stemming have been carried out by researchers in English and other major languages. In this paper, we study and review existing works on stemming in Bengali and other Indian languages. Finally, we propose a rule based approach that explores Bengali morphology and leverages WordNet to achieve better accuracy. Our algorithm produced stemming accuracy of 98.86% for Nouns and 99.75% for Verbs.
Anthology ID:
2020.icon-main.55
Volume:
Proceedings of the 17th International Conference on Natural Language Processing (ICON)
Month:
December
Year:
2020
Address:
Indian Institute of Technology Patna, Patna, India
Venue:
ICON
SIG:
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
400–408
Language:
URL:
https://aclanthology.org/2020.icon-main.55
DOI:
Bibkey:
Cite (ACL):
Souvick Das, Rajat Pandit, and Sudip Kumar Naskar. 2020. A Rule Based Lightweight Bengali Stemmer. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 400–408, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).
Cite (Informal):
A Rule Based Lightweight Bengali Stemmer (Das et al., ICON 2020)
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