Novel deep neural network framework for biomedical named entity recognition

Abstract

With the dramatic improvements in the field of bio-informatics, extracting information from text and analyzing the association between the entities has received more attention in the past few years. Entity Recognition (ER) meant to extract and recognize the entities from any text. Biomedical Named Entity Recognition (BNER) gets more and more attention from the researchers since it is a fundamental task in biomedical information extraction. Various methods has been proposed to perform the task of BioNER. Different kind of approaches are dictionary based, rule based approaches, traditional machine learning approaches that combines supervised and unsupervised methods and neural network based approach. The state-of-art systems previously adopted various supervised machine learning methods Hidden Markov Models (HMMs), Maximum Entropy Markov Models (MEMMs), Support vector machines(SVM),Structural Support Vector Machines(SSVMS),Conditional Random Fields(CRF) to derive semantic and syntatic features from annotated datasets. However, CRF is one of the most successful method used for NER and it has obtained finest result because of the robustness and ability for sequence lebelling task. Recently, studies have demonstrated the application of deep learning based approaches for biomedical named entity recognition (BioNER) and shown promising results.