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Analyzing Probability Vectors for Named Entity Statistical Machine Transliteration

by M. L. Dhore, S. K. Dixit, T. D. Sonwalkar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 55 - Number 10
Year of Publication: 2012
Authors: M. L. Dhore, S. K. Dixit, T. D. Sonwalkar
10.5120/8791-2776

M. L. Dhore, S. K. Dixit, T. D. Sonwalkar . Analyzing Probability Vectors for Named Entity Statistical Machine Transliteration. International Journal of Computer Applications. 55, 10 ( October 2012), 28-34. DOI=10.5120/8791-2776

@article{ 10.5120/8791-2776,
author = { M. L. Dhore, S. K. Dixit, T. D. Sonwalkar },
title = { Analyzing Probability Vectors for Named Entity Statistical Machine Transliteration },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 55 },
number = { 10 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 28-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume55/number10/8791-2776/ },
doi = { 10.5120/8791-2776 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:56:53.753958+05:30
%A M. L. Dhore
%A S. K. Dixit
%A T. D. Sonwalkar
%T Analyzing Probability Vectors for Named Entity Statistical Machine Transliteration
%J International Journal of Computer Applications
%@ 0975-8887
%V 55
%N 10
%P 28-34
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Machine transliteration systems are classified as either Rule-based methods or statistical methods. A rule-based method focuses on transliterating names using lots of human-made rules set. These systems are simple to implement but require huge amount of language expertise. In statistical methods, the importance is given in converting transliteration problem into a classification problem and employs a statistical model to solve this classification problem. Though these methods don't require expert knowledge of Language model, they need large amounts of bilingual data and good algorithm for training. Currently, basic Markov Chain Model (MM), Extended Markov Chain (EMC), Hidden Markov Model (HMM), Conditional Random Fields (CRF), Decision Tree (DT), Maximum Entropy Markov Model (MEMM) and Support Vector Machine (SVM) are the popular statistical approaches used by many researchers across the globe. This paper focuses on mathematical analysis of different statistical approaches used in machine transliteration of named entity which would be beneficial for many upcoming researchers to know the mathematics used behind the curtains.

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Index Terms

Computer Science
Information Sciences

Keywords

Conditional Random Fields Decision Trees Hidden Markov Model Markov Chain Statistical Machine Transliteration