Building Balinese Part-of-Speech Tagger Using Hidden Markov Model (HMM)

  • I Gde Made Hendra Pradiptha Universitas Udayana
  • Ngurah Agus Sanjaya ER Universitas Udayana

Abstract

Part-of-Speech tagging or word class labeling is a process for labeling a word class in a word in a sentence. Previous research on POS Tagger, especially for Indonesian, has been done using various approaches and obtained high accuracy values. However, not many researchers have built POS Tagger for Balinese. In this article, we are interested in building a POS Tagger for Balinese using a probabilistic approach, specifically the Hidden Markov Model (HMM). HMM is selected to deal with ambiguity since it gives higher accuracy and fast processing time. We used k-fold cross-validation (with k = 10) and tagged corpus around 3669 tokens with 21 tags. Based on the experiments conducted, the HMM method obtained an accuracy of 68.56%.

Downloads

Download data is not yet available.
Published
2020-11-24
How to Cite
PRADIPTHA, I Gde Made Hendra; SANJAYA ER, Ngurah Agus. Building Balinese Part-of-Speech Tagger Using Hidden Markov Model (HMM). JELIKU (Jurnal Elektronik Ilmu Komputer Udayana), [S.l.], v. 9, n. 2, p. 303-308, nov. 2020. ISSN 2654-5101. Available at: <https://ojs.unud.ac.id/index.php/jlk/article/view/64491>. Date accessed: 28 may 2024. doi: https://doi.org/10.24843/JLK.2020.v09.i02.p18.