Open AccessFilipino Native Language Identification using Markov Chain Model and Maximum Likelihood Decision Rule
Author(s) -
Ria A. Sagum
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i3.2206
Subject(s) - identification (biology) , computer science , markov chain , hidden markov model , artificial intelligence , natural language processing , language model , maximum likelihood , markov model , language identification , linguistics , statistics , machine learning , mathematics , natural language , philosophy , botany , biology
The study developed a tool for identification of a Filipino Native Language given a textual data. The Filipino Language identified were Cebuano, Kapampangan and Pangasinan. It used Markov Chain Model for language modeling using bag of words (a total of 35,144 words for Cebuano, 14752 for Kapampangan, and 13969 of Pangasinan) from each language and maximum likelihood decision rule for the identification of the native language. The obtained model implementing Markov model, was applied in one hundred fifty text files with minimum length of ten words and maximum length of fifty words. The result of the evaluation shows the system’s accuracy of 86.25% and an F-Score of 90.55%.