Comparing naive bayes and support vector machines on Sarawak gazette named entity recognition

• Main Author: Wan Muhammad Faisal, Wan Tamlikha
• Format: Final Year Project Report
• Language: English; English
• Published: Universiti Malaysia Sarawak, (UNIMAS) 2015
• Subjects: T Technology (General)
• Online Access: http://ir.unimas.my/id/eprint/38981/1/WAN%20MUHAMMAD%20FAISAL%20%2824%20pgs%29.pdf; http://ir.unimas.my/id/eprint/38981/2/WAN%20MUHAMMAD%20FAISAL%20%28fulltext%29.pdf; http://ir.unimas.my/id/eprint/38981/

This paper presents the report of Final Year Project 2 Comparing Naive Bayes and Support Vector Machines on Sarawak Gazette Named Entity Recognition. The need to annotate automatically the Sarawak Gazette is essential to allow the SAGA searchable through Named Entities (NEs). Hence, this paper objective is to apply Naive Bayes on the Sarawak Gazette for Named entity recognition along with Support Vector Machine to compare the accuracy of Naive Bayes and Support Vector Machine techniques on Sarawak Gazette named entity recognition.. Moreover, this paper also reviews and analyzes related papers from other researchers regarding the implementation of Supervised Machine Learning to find the best technique to annotate SAGA. A methodology is introduced to explain the flow of the project and the element it carry. This project is implemented in WEKA environment software. The comparison is done after conducting various test method to find the most accurate. The result is compare and analyze.