@article{(Open Science Index):https://publications.waset.org/pdf/12114, title = {Addressing Scalability Issues of Named Entity Recognition Using Multi-Class Support Vector Machines}, author = {Mona Soliman Habib}, country = {}, institution = {}, abstract = {This paper explores the scalability issues associated with solving the Named Entity Recognition (NER) problem using Support Vector Machines (SVM) and high-dimensional features. The performance results of a set of experiments conducted using binary and multi-class SVM with increasing training data sizes are examined. The NER domain chosen for these experiments is the biomedical publications domain, especially selected due to its importance and inherent challenges. A simple machine learning approach is used that eliminates prior language knowledge such as part-of-speech or noun phrase tagging thereby allowing for its applicability across languages. No domain-specific knowledge is included. The accuracy measures achieved are comparable to those obtained using more complex approaches, which constitutes a motivation to investigate ways to improve the scalability of multiclass SVM in order to make the solution more practical and useable. Improving training time of multi-class SVM would make support vector machines a more viable and practical machine learning solution for real-world problems with large datasets. An initial prototype results in great improvement of the training time at the expense of memory requirements.}, journal = {International Journal of Computer and Information Engineering}, volume = {2}, number = {1}, year = {2008}, pages = {17 - 26}, ee = {https://publications.waset.org/pdf/12114}, url = {https://publications.waset.org/vol/13}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 13, 2008}, }