Part of Speech Tagging for Indonesian Language using Bidirectional Long Short-Term Memory

Handrata, Dellon; Purwanto, Christian Nathaniel; Chandra, Fransisca Haryanti; Santoso, Joan; Gunawan, .

doi:10.1109/icoris.2019.8874871

Cited by 5 publications

(2 citation statements)

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“…The accuracy of each result obtained from the 10-fold cross-validation is calculated by comparing the results with the original data. Accuracy can be obtained using (10) [31], [32]. Then the accuracy between MEMM Bigram and MEMM Trigram is compared so that it is known which method is better.…”

Section: Evaluation and Analysismentioning

confidence: 99%

“…Previous research related to POS tagging was also carried out using HMM which resulted an accuracy rate of 96.50% [9]. Then, bidirectional long short-term memory produces an accuracy rate of 96.92% [10]. Other research related to POS tagging that has been conducted using the deep neural network for Turkish produces an accuracy rate of 88.7% [11], deep learning for Nepali produces an accuracy of 99% [12], maximum entropy for English produces an accuracy of 96.6% [13], HMM for Azerbaijani language yields an accuracy of 90% [14], HMM and morphological rules for Myanmar language get 94% precision [15], and CRF and Bi-LSTM for the arabic tweets get 96.5% accuracy [16].…”

Section: Introductionmentioning

confidence: 99%

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Indonesian part of speech tagging using maximum entropy markov model on Indonesian manually tagged corpus

Cahyani

Mustikaningtyas

2022

IJ-AI

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This research discusses the development of a part of speech (POS) tagging system to solve the problem of word ambiguity. This paper presents a new method, namely maximum entropy markov model (MEMM) to solve word ambiguity on the Indonesian dataset. A manually labeled “Indonesian manually tagged corpus” was used as data. Furthermore, the corpus is processed using the entropy formula to obtain the weight of the value of the word being searched for, then calculating it into the MEMM Bigram and MEMM Trigram algorithms with the previously obtained rules to determine the part of speech (POS) tag that has the highest probability. The results obtained show POS tagging using the MEMM method has advantages over the methods used previously which used the same data. This paper improves a performance evaluation of research previously. The resulting average accuracy is 83.04% for the MEMM Bigram algorithm and 86.66% for the MEMM Trigram. The MEMM Trigram algorithm is better than the MEMM Bigram algorithm.

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Section: Evaluation and Analysismentioning

confidence: 99%