LITERATURE REVIEW INFORMATION EXTRACTION PADA TEKS BIOMEDICAL DENGAN PENDEKATAN MACHINE LEARNING DAN NAMED ENTITY RECOGNITION
DOI:
https://doi.org/10.56127/juit.v2i1.1727Keywords:
Biomedical, Information Extraction, Machine Learning, Named Entity RecognitionAbstract
Extraction of information or information extraction is a field of science for natural language processing, by converting unstructured into information in a structured form. Information that is useful and requires data that can be used as a reference in making decisions. Machine Learning Algorithms have been used to build basic classification models to identify pediatrics in biomedical texts and have been developed to provide more accurate information. Named Entity Recognition (NER) is useful to help identify and identify a word. The biomedical field has a lot of literature so that NER is highly demanded in the biomedical domain. Biomedical texts are not homogeneous. Medical notes or records written differently from scientific articles, sequence annotations, or other recent public health. Various methods in biomedicine can be recognized, the meaning of which has been developed using machine learning. This study discusses previous research on NER in the biomedical sector.
References
Chincor, N., Brown, E., Ferro, L., and Robinson, P. “Named Entity Task Definition”, Version 1.4 The MITRE Corporation and SAIC. 1999.
Chincor, N., Brown, E., Ferro, L., and Robinson, P. MUC-7 Information Extraction Task Definition, The MITRE Corporation and SAIC. 1998.
Daniel M. Bikel, Scott Miller, Richard Schwartz and Ralph Weischedel. “Nymble: a highperformance learning name-finder”, in the proceedings of the fifth conference on Applied natural language processing, pages 194-201, San Francisco, CA, USA Morgan Kaufmann Publishers Inc. 1997.
Andrew Borthwick. “Maximum Entropy Approach to Named Entity Recognition” Ph.D. thesis, New York University. 1999.
Hideki Isozaki. “Japanese named entity recognition based on a simple rule generator and decision tree learning” in the proceedings of the Association for Computational Linguistics, pages 306-313. India. 2001.
Takeuchi K., and Collier N. “Use of Support Vector Machines in extended named entity recognition” in the proceedings of the sixth Conference on Natural Language Learning (CoNLL-2002), Taipei, Taiwan, China. 2002.
John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. “Conditional Random Fields:Probabilistic Models for Segmenting and Labeling Sequence Data” in the proceedings of International Conference on Machine Learning, pages 282-289, Williams College, Williamstown, MA, USA. 2001.
Kaur, D., and Gupta, V. “A Survey of Named Entity Recognition in English and othe Indian Language”, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 6, ISSN (Online): 1694-0814. 2010.
Mansouri, L. S. Affendey, A Mamat. “Named Entity Recognition Approaches”, IJCSNS International Journal of Computer Science and Network Security, Vol.8 No.2. 2008.
Zhang, J., Shen, D., Zhou, G., Su, J., and Tan, CL., “Enhancing HMM-based biomedical named entity recognitionby studying special phenomena”, Journal of Biomedical Informatics, Vol. 37, pages 411-422. 2004.
Lee, Ki-Joong., Hwang, Young-Sook., Kim, S., and Rim, Hae-Chang. “Biomedical named entity recognition using two-phase model based on SVMs”, Journal of Biomedical Informatics Vol. 37, Issue 6, Dec, pp. 436-447, https://doi.org/10.1016/j.jbi.2004.08.012. 2004.
Finkel, J., Dingare, S., Manning, CD., Nissim, M., Alex, B., and Glover, C. “Exploring the boundaries: gene and protein identification in biomedical text”, BMC Bioinformatics, doi:10.1186/1471-2105-6-S1-S5. 2005.
Goulart, R. R. V., Lima, V. L. S. d., and Xavier, C. “A systematic review of named entity recognition in biomedical texts”, Journal of the Brazilian Computer Society 17 (2) pages 103-116. DOI: 10.1007/s13173-011-0031-9. 2011.
Alshaikhdeeb, B., and Ahmad, K. “Biomedical Named Entity Recognition: A Review”, International Journal on Advanced Science Engineering and Information Technology, Vol. 6, No. 6, pages. 889-895. ISSN. 2088-53342016. 2016.
Wulandari, DW., Adikara, PP., dan Adinugroho, S. “Named Entity Recognition (NER) Pada Dokumen Biologi Menggunakan Rule Based dan Naïve Bayes Classifier”, Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, Vol. 2, No. 11, Hlm. 4555-4563. ISSN. 2548-9646X. 2018.
Lee, W., Khuri, N., and Wu, M. “Information Extraction from Biomedical Text Using Machine Learning”, Master’s Theses and Graduate Research, San Jose State University. 2019.
