MedNLU: Natural Language Understander for Medical Texts

Abstract

Natural Language Understanding is one of the essential tasks for building clinical text-based applications. Understanding of these clinical texts can be achieved through Vector Space Models and Sequential Modelling tasks. This paper is focused on sequential modelling i.e. Named Entity Recognition and Part of Speech Tagging by attaining a state of the art performance of 93.8% as F1 score for i2b2 clinical corpus and achieves 97.29% as F1 score for GENIA corpus. This paper also states the performance of feature fusion by integrating word embedding, feature embedding and character embedding for sequential modelling tasks. We also propose a framework based on a sequential modelling architecture, named MedNLU, which has the capability of performing Part of Speech Tagging, Chunking, and Entity Recognition on clinical texts. The sequence modeler in MedNLU is an integrated framework of Convolutional Neural Network, Conditional Random Fields and Bi-directional Long-Short Term Memory network. © 2020, Springer Nature Switzerland AG.