Suryana, Agus and Ipnuwati, Sri (2016) Vector Machine Learning Method for Text Mining Indonesian Social Media Named Entity Recognition. The 5th International Conference on Information Technology and Engineering Application (ICIBA2016).
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Abstract
Social media named entity recognition (SMNER) is one of the important tasks in social media information extraction, which involves the identification and classification of words or sequences of words denoting a concept or entity. With the extension of named entity recognition to new information areas, the task of identifying meaningful entities has become more complex as categories are more specific to a given domain. SMNER solutions that achieve a high level of accuracy in some language or domain may perform much poorly in a different context. Support Vector Machine (SVM) is rapidly emerging as a promising pattern recognition methodology due to its generalization capability and its ability to handle high dimensional input. However, SVM is known to suer from slow training especially with large input data size. In this paper, we explore the scalability issues for Indonesian social media named entity recognition using high-dimensional features and support vector machines.
| Item Type: | Article |
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| Uncontrolled Keywords: | Support Vector Machine, Machine Learning, Text Mining, Indonesian Social Media, Named Entity Recognition |
| Subjects: | A General Works > AI Indexes (General) T Technology > T Technology (General) |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
| Depositing User: | AssocProf. Leon Abdillah |
| Date Deposited: | 28 Mar 2016 00:29 |
| Last Modified: | 28 Mar 2016 00:29 |
| URI: | http://eprints.binadarma.ac.id/id/eprint/2776 |
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