Scalable biomedical Named Entity Recognition: investigation of a database-supported SVM approach.
This paper explores scalability issues associated with the Named Entity Recognition problem in the biomedical publications domain using Support Vector Machines. The performance results using existing binary and multi-class SVMs with increasing training data are compared to results obtained using our new implementations. Our approach eliminates prior language or domain-specific knowledge and achieves good out-of-the-box accuracy measures comparable to those obtained using more complex approaches. The training time of multi-class SVMs is reduced by several orders of magnitude, which would make support vector machines a more viable and practical solution for real-world problems with large datasets.
Cairo Microsoft Innovation Lab, 306 Korniche El-Nile, Maadi Cairo, Egypt. mona.habib@microsoft.com
Mona Soliman
MS
Habib
Jugal
J
Kalita
D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D010363:Pattern Recognition, Automated / Q000379:methods