On active annotation for named entity recognition

A major constraint of machine learning techniques for solving several information extraction problems is the availability of sufficient amount of training examples, which involve huge costs and efforts to prepare. Active learning techniques select informative instances from the unlabeled data and add it to the training set in such a way that the overall classification performance improves. In random sampling approach, unlabeled data is selected for annotation at random and thus can’t yield the desired results. In contrast, active learning selects the useful data from a huge pool of unlabeled documents. The strategies used often classify the instances to belong to the incorrect classes. The classifier is confused between two classes if the test instance is located near the margin. We propose two methods for active learning, and show that these techniques favorably result in the increased performance. The first approach is based on support vector machine (SVM), whereas the second one is based on an ensemble learning which utilizes the classification capabilities of two well-known classifiers, namely SVM and conditional random field. The motivation of using these classifiers is that these are orthogonal in nature, and thereby a combination of them can produce the better results. In order to show the efficacy of the proposed approach we choose a crucial problem, namely named entity recognition (NER) in three languages, namely Bengali, Hindi and English. This is also evaluated for NER in biomedical domain. Evaluation results reveal that the proposed techniques indeed show considerable performance improvements.