Chemical Named Entity Recognition using Undersampling and Classifier Ensembles

Chemical Named Entity Recognition (ChemNER) is the first step for a large number of consequent Information Extraction (IE) tasks in the chemistry related sciences and drug development domains. Extraction of drug-drug interactions, chemical compounds‘ resolution, and creation of question answering systems are examples of such applications. Any improvement in the quality of NER process in this context may affect the performance of subsequent tasks which shows the importance of this preliminary step in IE applications. In this thesis we studied this problem by proposing a modular architecture to improve the performance of ChemNER systems. This thesis has three main contributions to the overall task. The first contribution is the design of a new rule based tokenizer which improves the quality of data preprocessing phase. Due to the highly imbalanced nature of the data used in the NER task, overall performance of the classifiers used is usually not as good as those used in some other common classification tasks. Hence, a new sentence based undersampling approach specifically to be used for the NER problems is proposed as the second contribution for the given problem. The proposed undersampling approach tries to remove the insignificant samples from the training data aiming at preserving the structure of the given sentences as much as possible. We name it as Balance Undersampling (BUS) approach since it tries to keep almost an equal number of negative samples surrounding the positives. The third contribution of this thesis is to use the Particle Swarm Optimization algorithm as a heuristic classifier selection method together with the Naïve Bayesian combination approach to form a classifier ensemble from a large pool of classifiers created using undersampled data with different sampling ratios and various feature sets. All experiments during this study are conducted using the BioCreative IV ChemDNER corpus which is the most comprehensive data set in the domain.