Open Access

ARTICLE

An Accurate Persian Part-of-Speech Tagger

Morteza Okhovvat^1,∗, Mohsen Sharifi^2,†, Behrouz Minaei Bidgoli^2,‡

1 Health Management and Social Development Research Center, Golestan University of Medical Sciences, Gorgan, Iran
2 School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
† Mohsen Sharifi, msharifi@iust.ac.ir
‡ Behrouz Minaei Bidgoli, b_minaei@iust.ac.ir

* Corresponding Author: Morteza Okhovvat,

Computer Systems Science and Engineering 2020, 35(6), 423-430. https://doi.org/10.32604/csse.2020.35.423

Abstract

The processing of any natural language requires that the grammatical properties of every word in that language are tagged by a part of speech (POS) tagger. To present a more accurate POS tagger for the Persian language, we propose an improved and accurate tagger called IAoM that supports properties of text to speech systems such as Lexical Stress Search, Homograph words Disambiguation, Break Phrase Detection, and main aspects of Persian morphology. IAoM uses Maximum Likelihood Estimation (MLE) to determine the tags of unknown words. In addition, it uses a few defined rules for the sake of achieving high accuracy. For tagging the input corpus, IAoM uses a Hidden Markov Model (HMM) alongside the Viterbi algorithm. To present a fair evaluation, we have performed various experiments on both homogeneous and heterogeneous Persian corpora and studied the effect of the size of training set on the accuracy of IAoM. Experimental results demonstrate the merit of the proposed tagger in achieving an overall accuracy of 97.6%.

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Citations