Minimizing Human Labelling Effort for Annotating Named Entities in Historical Newspaper

Wan Tamlikha, W.M.F. and Ranaivo-Malançon, Bali and Chua, S. (2017) Minimizing Human Labelling Effort for Annotating Named Entities in Historical Newspaper. Journal of Telecommunication, Electronic and Computer Engineering, 9 (2-10). ISSN 2289-8131

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To accelerate the annotation of named entities (NEs) in historical newspapers like Sarawak Gazette, only two choices are possible: an automatic approach or a semi-automatic approach. This paper presents a fully automatic annotation of NEs occurring in Sarawak Gazette. At the initial stage, a subset of the historical newspapers is fed to an established rule-based named entity recognizer (NER), that is ANNIE. Then, the preannotated corpus is used as training and testing data for three supervised learning NER, which are based on Naïve Bayes, J48 decision trees, and SVM-SMO methods. These methods are not always accurate and it appears that SVM-SMO and J48 have better performance than Naïve Bayes. Thus, a thorough study on the errors done by SVM-SMO and J48 yield to the creation of ad hoc rules to correct the errors automatically. The proposed approach is promising even though it still needs more experiments to refine the rules.

Item Type: Article
Additional Information: Information, Communication and Creative Technology
Uncontrolled Keywords: J48, Naïve Bayes, Named Entity, Sarawak Gazette, SVM-SMO, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Karen Kornalius
Date Deposited: 27 Sep 2018 07:19
Last Modified: 29 Sep 2022 04:30

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