Sri Agustini, Dian (2022) Vector Machine Learning Method for Text Mining Indonesian Social Media Named Entity Recognition. Vector Machine Learning Method for Text Mining Indonesian Social Media Named Entity Recognition.
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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 classifica�tion 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 mean�ingful 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 suffer 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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| Subjects: | H Social Sciences > H Social Sciences (General) |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Civil Engineering and the Environment |
| Depositing User: | Mr Edi Surya Negara |
| Date Deposited: | 05 Jul 2022 04:36 |
| Last Modified: | 05 Jul 2022 04:36 |
| URI: | http://eprints.binadarma.ac.id/id/eprint/17140 |
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