Named Entity Recognition and Knowledge Extraction from Pharmaceutical Texts using Transfer Learning
Journal
Procedia Computer Science
Date Issued
2022-01-01
Author(s)
Jofche, Nasi
Jovanovik, Milos
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.
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.
Subjects
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