Bio-NER: Biomedical Named Entity Recognition using Rule-Based and Statistical Learners

Abstract

The purpose of extracting of Bio-Medical Entities is to recognize the particular entities, whether word or phrases, from the unstructured data contained in the text. This work proposes different approaches and methods, i.e. Machine Learning Hybrid Classification, Rule Based Non-tested Generalized Exemplars and Partial Decision Tree (PART) Learners for Bio-Medical Named Entity Recognition. The Prime objective is to consider, preferably, simple characteristics, such as, affixes and context. In addition, orthographic, Parts of Speech (POS) tags and N-grams are given secondary importance as for as their comparison with affixes and context is concerned. Further, for the very purpose of Bio-medical Diseased Named Recognition, proposal of Rule Based Classifiers along with the Statistical Machine Learning is given. Also, this paper proposes the blend of both preceding methods that jointly construct Hybrid Classification algorithm. Precision, Recall and F-measure - standard metrics- has been put into practice for the evaluation. The results prove that the technique used has far better performance results than the method used before - state-of-art Disease NER (Named Entity Recognition).