Evaluation of Part of Speech Tagger Approaches for the Amharic Language: A Review
Wubetu Barud Demilie, Ayodeji Olalekan Salau, Kiran Kumar Ravulakollu · 2022
Accurately tagging correct grammar for individual words in a sentence is a critical task for natural language processing applications. Different deep and machine learning-oriented approaches to Part of Speech Tagger (POST) have recently been deployed as promising methods for identifying words in a phrase or sentence. This work presents the detailed concepts of POST research work on the Amharic language. Additionally, a comprehensive comparison of well-known deep and machine learning-oriented approaches was used in the development and implementation of POST for the language. A complete assessment of all published POST research works on the language is presented, together with a discussion of the proposed methods' performance, with a remark. Then, in terms of the recommended methodologies used and their performance evaluation criteria, recent developments and advancements in deep and machine learning oriented parts of speech taggers are described. Finally, we gave future recommendations for study in developing deep and machine learning-oriented POST using the results of the proposed methodologies based on their performances.