Ensemble based active annotation for biomedical named entity recognition

Active Learning is an important prospect of machine learning for information extraction to deal with the problems of high cost of collecting labeled examples. It makes more efficient use of the learner´s time by asking them to label only instances that are most useful for the trainer. We propose a novel method for solving this problem and show that it favorably results in the increased performance. Our proposed framework is based on an ensemble approach, where Decision Tree and Memory-based Learner are used as the base learners. The proposed approach is applied for solving the problem of named entity recognition (NER) in biomedical domain. Results show that the proposed technique indeed improves the performance of the system significantly.