Tagging Units in the Text and the Bayes Algorithm

This article presents an analysis of the methods employed for the tagging of units within textual data. The stages of automatic tagging of language units, in particular slangs, are covered in detail based on the working theory of the Bayesian algorithm. The factors that contribute to an increased accuracy of the calculated probability are outlined. And also this article analyzes the advantages and disadvantages of the Bayesian algorithm for text unit tagging. The steps of the algorithm are elucidated with the aid of illustrative examples. Describes the characteristics of the Bayesian algorithm as a computational method for estimating the probability of an object, its characteristics, and the group to which it belongs. This method has been shown to provide accurate results in data analysis using machine learning methods for automatic tagging of jargon, the necessity of distinguishing and automatic classification of lexical units in the language corpus, its importance in solving the problems related to the confusion in the analysis of the text containing such units.

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