Open AccessPOS Tagging Bahasa Indonesia Dengan HMM dan Rule Based
Author(s) -
Kathryn Widhiyanti,
Agus Harjoko
Publication year - 2013
Publication title -
jurnal informatika
Language(s) - English
Resource type - Journals
ISSN - 1693-7279
DOI - 10.21460/inf.2012.82.125
Subject(s) - computer science , hidden markov model , natural language processing , artificial intelligence , part of speech , word (group theory) , sentence , labelling , class (philosophy) , parsing , indonesian , part of speech tagging , speech recognition , linguistics , psychology , philosophy , criminology
The research conduct a Part of Speech Tagging (POS-tagging) for text in Indonesian language, supporting another process in digitising natural language e.g. Indonesian language text parsing. POS-tagging is an automated process of labelling word classes for certain word in sentences (Jurafsky and Martin, 2000). The escalated issue is how to acquire an accurate word class labelling in sentence domain. The author would like to propose a method which combine Hidden Markov Model and Rule Based method. The expected outcome in this research is a better accurary in word class labelling, resulted by only using Hidden Markov Model. The labelling results –from Hidden Markov Model– are refined by validating with certain rule, composed by the used corpus automatically. From the conducted research through some POST document, using Hidden Markov Model, produced 100% as the highest accurary for identical text within corpus. For different text within the referenced corpus, used words subjected in corpus, produced 92,2% for the highest accurary.