Automatic Part-of-Speech Tagging

Manolea Adelina ()
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Manolea Adelina: master student in the Embedded Systems program, Faculty of Engineering, “Lucian Blaga” University of Sibiu, Romania

International Journal of Advanced Statistics and IT&C for Economics and Life Sciences, 2024, vol. 14, issue 1, 197-203

Abstract: Natural language processing (NLP) is a key technique in Business Process Management (BPM). The performance of BPM methods, which are based on NLP, is limited by the accuracy of automatic part-of-speech tagging, a base subtask of NLP.[9] The automatic part-of-speech tagging is the process of assigning a tag to every word in a text or a document.[1] I have developed and presented in this paper an application that learns to correctly predict parts-of-speech for words within a sentence using a machine learning algorithm. For this I used a pre-labeled data set (Brown Corpus) and implemented, evaluated and compared several versions of the n-Gram algorithm with the aim of obtaining the best classification accuracy of the automatic part-of-speech tagging process.

Keywords: part-of-speech tagging; n-Gram language model; text normalization (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:ijsiel:v:14:y:2024:i:1:p:197-203:n:1004

DOI: 10.2478/ijasitels-2024-0004

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