Scalable biomedical Named Entity Recognition: investigation of a database-supported SVM approach

This paper explores the scalability issues associated with solving the Named Entity Recognition (NER) problem using Support Vector Machines (SVM) and high-dimensional features and presents two implementations to address these issues. The NER domain chosen for these experiments is the biomedical publications domain, especially selected due to its importance and inherent challenges. The performance results of a set of experiments conducted using existing binary and multiclass SVM with increasing training data sizes are examined and compared to results obtained using our new implementations. Our baseline machine learning approach eliminates prior language or domain-specific knowledge and achieves good outof-the-box accuracy measures that are comparable to those obtained using more complex approaches. The training time of multi-class SVM is reduced by several orders of magnitude, which would make support vector machines a more viable and practical machine learning solution for real-world p...

• Type: Journal
• Year: 2010
• Where: IJBRA
• Authors: Mona Soliman Habib, Jugal Kalita
• Keywords: IJBRA 2010, Machine, Multi-class Svm, Support Vector