Named Entity Recognition and Knowledge Extraction from Pharmaceutical Texts using Transfer Learning

Abstract

The challenge of recognizing named entities in a given text has been a very dynamic field in recent years. This task is generally focused on tagging common entities, such as Person, Organization, Date, etc. However, many domain-specific use-cases exist which require tagging custom entities that are not part of the pre-trained models. This can be solved by fine-tuning the pre-trained models or training custom models. The main challenge lies in obtaining reliable labeled training and test datasets, and manual labeling would be a highly tedious task. This paper presents a text analysis platform focused on the pharmaceutical domain. We perform text classification using state-ofthe-art transfer learning models based on spaCy, AllenNLP, BERT, and BioBERT. We developed methodology that is used to create accurately labeled training and test datasets used for custom entity labeling model fine-tuning. Finally, this methodology is applied in the process of detecting Pharmaceutical Organizations and Drugs in texts from the pharmaceutical domain. The obtained F1 scores are 96.14% for the entities occuring in the training set, and 95.14% for the unseen entities, which is noteworthy compared to other state-of-the-art methods. The proposed approach implemented in the platform could be applied in mobile and pervasive systems since it can provide more relevant and understandable information to patients by allowing them to scan the medication guides of their drugs. Furthermore, the proposed methodology has a potential application in verifying whether another drug from another vendor is compatible with the patient’s prescription medicine. Such approaches are the future of patient empowerment.