2026-04-14T08:08:43Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/00006744 2021-12-13T05:00:49Z 1582963342780:1596102355557 user-uit

Comparative Study of Naïve Bayesian Classifier and Transformation-Based Learning for Myanmar Function Tagging Win Win Thant Tin Myat Htwe Ni Lar Thein This paper describes the use of two machine learning techniques, Naive Bayesian classifier (NB) and transformation-based learning (TBL), to address the task of assigning function tags to Myanmar sentences. Function tagging is a process of assigning syntactic categories like subject, object, time and location to each word in the text document. It is an important step in Natural Language Processing. Function tags can help to improve the performance of Myanmar to English machine translation system. In this paper, we present a comparison of two methods in our experiments. The results showed that TBL was better and outperformed NB and there was a slight difference between the results. 2012-04-29 https://meral.edu.mm/records/6744