Named Entity Recognition for Characteristic of Medical Herbs Using Modified HMM Approach

  • Lailil Muflikhah Department of Computer Science Brawijaya University Malang, East Java, Indonesia
  • Agung Setiyono Faculty of Computer Science; Brawijaya University; Malang, Indonesia
  • Nurul Hidayat Faculty of Computer Science; Brawijaya University; Malang, Indonesia
Keywords: Hidden Marcov Model, Gazetteer, Viterbi, Named entity, Medicinal herbs

Abstract

The amount of articles in medicinal herbs is very huge. It is performed with unstructured format so that it takes time to get information as reader’s need. Therefore, this research purposes to recognize the name entity of article from internet in order to increase information retrieval or other analysis data purposes. Named entity recognition is one of the goals of information extraction which is to identify the name and characteristics of the herbs. This paper is propose the modified method of Hidden Marcov Model (HMM) with Viterbi algorithm. In this method, it is enclosed gazetteer list for labeling name and location of data training to construct HMM. The data sets are taken from three web sites including: miliaton, aliweb, and plants. As a result, the performance is achieved at average precision value of 0.93, recall of 0.83 and f-measure of 0.85.

References

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Published
2019-03-08
How to Cite
Muflikhah, L., Setiyono, A., & Hidayat, N. (2019). Named Entity Recognition for Characteristic of Medical Herbs Using Modified HMM Approach. Transactions on Machine Learning and Artificial Intelligence, 7(1), 50. https://doi.org/10.14738/tmlai.71.6086