Benchmarking Mi-POS: Malay Part-of-Speech Tagger

Abstract

A part-of-speech tagger as signs the correct grammatical category to each word in a given text based on the context surrounding the word. This paper presents Mi-POS, a Malay language Part-of-Speech tagger that is developed using a probabilistic approach with information about the context. The results of benchmarking Mi-POS against several similar systems are also presented in this paper and the lessons learnt from it are highlighted. The dataset used for evaluation consists of manually annotated texts. The authors used the accuracy and time to measure the results of this evaluation. The final results show that Mi-POS outperforms other Malay Part-of-Speech taggers in terms of accuracy with an accuracy of 95.16% obtained by tagging new words from the same training corpus type and 81.12% for words from different corpora types