A Literature Research on Machine Learning Techniques used for Training Annotated Corpus
Fitrah Rumaisa1, Halizah Basiron2, Zurina Saaya3, Noorli Khamis4
1Fitrah Rumaisa, Department of Information Technology, Universitas Widyatama, JlCikutra A, Bandung Indonesia.
2Halizah Basiron, Fakulti Teknologi Maklumatdan Komunikasi (FTMK), UniversitiTeknikal Malaysia Melaka (UTeM), Melaka Malaysia.
3ZurinaSaaya, Fakulti Teknologi Maklumatdan Komunikasi (FTMK), UniversitiTeknikal Malaysia Melaka (UTeM), Melaka Malaysia.
4Noorli Khamis, Pusat Bahasadan Pembangunan Insan (PBPI), Universiti Teknikal Malaysia Melaka (UTeM), Melaka Malaysia.
Manuscript received on 21 August 2019 | Revised Manuscript received on 11 September 2019 | Manuscript Published on 17 September 2019 | PP: 1331-1337 | Volume-8 Issue-2S8 August 2019 | Retrieval Number: B10630882S819/2019©BEIESP | DOI: 10.35940/ijrte.B1063.0882S819
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The development of research in the annotation area is growing. Researchers perform annotation task using various forms of datasets such as text, sound, images, and videos. Various algorithms are used to perform tasks. The purpose of this survey is to find out algorithms that are often used by researchers to perform annotation tasks, especially on text data. The literature surveys thirteen research papers on text annotation from the last 5 years. The results of this review indicate that SVM is the algorithm used for all three annotation methods: manual, automatic and semi-automatic annotation, with a significant accuracy above 80%. The result of this survey will be referred by the authors as the basis for subsequent research that will be conducted, especially in the semi-automatic annotation method.
Keywords: Annotation, Algorithm, Text Survey, Semi-Automatic.
Scope of the Article: Machine Learning