@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},
	}