A corpus based unsupervised Bangla word stemming using N-gram language model
Tapashee Tabassum Urmi, Jasmine Jahan Jammy, Sabir Ismail · 2016
In this paper, we propose a contextual similarity based approach for identification of stems or root forms of Bangla words using N-gram language model. The core purpose of our work is to build a big corpus of Bangla stems with their corresponding inflectional forms. Identification of stem form of a word is generally called stemming and the tool which identifies the stems is called stemmer. Stemmers are important mainly in information retrieval systems, recommending systems, spell checkers, search engines and other sectors of Natural Language Processing applications. We selected N-gram model for stem detection based on the assumption that if two words which exhibit a certain percentage of similarity in spelling and have a certain percentage of contextual similarity in many sentences then these words have higher probability of originating from the same root. We implemented 6-gram model for the stem identification procedure and we gained 40.18% accuracy for our corpus.