Morteza Okhovvat1,∗, Mohsen Sharifi2,†, Behrouz Minaei Bidgoli2,‡
Computer Systems Science and Engineering, Vol.35, No.6, pp. 423-430, 2020, DOI: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 More >
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