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Review of Stochastic POS Tagging Techniques used in Bengali

by Abul Kalam Md. Rajib Hasan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 102 - Number 8
Year of Publication: 2014
Authors: Abul Kalam Md. Rajib Hasan
10.5120/17838-8724

Abul Kalam Md. Rajib Hasan . Review of Stochastic POS Tagging Techniques used in Bengali. International Journal of Computer Applications. 102, 8 ( September 2014), 35-39. DOI=10.5120/17838-8724

@article{ 10.5120/17838-8724,
author = { Abul Kalam Md. Rajib Hasan },
title = { Review of Stochastic POS Tagging Techniques used in Bengali },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 102 },
number = { 8 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 35-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume102/number8/17838-8724/ },
doi = { 10.5120/17838-8724 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:32:36.755240+05:30
%A Abul Kalam Md. Rajib Hasan
%T Review of Stochastic POS Tagging Techniques used in Bengali
%J International Journal of Computer Applications
%@ 0975-8887
%V 102
%N 8
%P 35-39
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we describe different stochastic methods or techniques used for POS tagging of Bengali language. We have shown a generalized stochastic model for POS tagging in Bengali. We reviewed kinds of corpus and number of tags used for tagging methods. In the study it is found that as many as 45 useful tags existed in the literature. There are four useful corpus found in the study. As Bengali is a morphologically rich language we outlined a feature list that could be used with different training algorithms. We found that a hybrid HMM model used with a morphological analyzer work best in Bengali with an accuracy of 96. 3%.

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Index Terms

Computer Science
Information Sciences

Keywords

Natural Language Processing (NLP) Machine Learning.

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