Abstract
Identifying the character entities correctly in a story becomes extremely challenging since an entity can refer to a proper noun, a phrase, or a particular definition. This study proposes BPSO-CRF, a hybrid NER method to extract character entities in Balinese stories. In addition, we develop a training dataset for balinese character named entities recognition task. We compare the proposed method against three baseline methods. Overall, BPSO-CRF obtains a relatively better recognition rate compared to the baseline method. Furthermore, only a few numbers of contextual features are relevant to improve the performance of the baseline CRF model.
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Notes
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HMM-based POS Tagging for balinese text was retrieved from https://pypi.org/project/balinese-library/
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Acknowledgments
This research was supported by the Udayana University's Institute for Research and Community Service (LPPM Unud), under Udayana’s Invention Research (Penelitian Invensi Udayana) scheme and grant number B/1.3/UN14.4.A/PT.01.03/2023, May 2nd, 2023.
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Bimantara, I.M.S., Sanjaya ER, N., Purwitasari, D. (2023). Character Entity Recognition Using Hybrid Binary-Particle Swarm Optimization and Conditional Random Field on Balinese Folklore Text. In: Delir Haghighi, P., et al. Information Integration and Web Intelligence. iiWAS 2023. Lecture Notes in Computer Science, vol 14416. Springer, Cham. https://doi.org/10.1007/978-3-031-48316-5_15
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DOI: https://doi.org/10.1007/978-3-031-48316-5_15
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