Review of Natural Language Processing in Pharmacology

TL;DR

This paper surveys recent advances in natural language processing applied to pharmacology, highlighting its role in information extraction, drug interaction detection, and various NLP methodologies and tools.

Contribution

It provides a comprehensive overview of NLP applications, datasets, and tools in pharmacology, categorizing recent developments and summarizing key methodologies and tasks.

Findings

NLP is extensively used for information extraction in pharmacology.

Deep neural networks have advanced drug interaction detection.

The survey categorizes NLP methods, datasets, and tools relevant to pharmacology.

Abstract

Natural language processing (NLP) is an area of artificial intelligence that applies information technologies to process the human language, understand it to a certain degree, and use it in various applications. This area has rapidly developed in the last few years and now employs modern variants of deep neural networks to extract relevant patterns from large text corpora. The main objective of this work is to survey the recent use of NLP in the field of pharmacology. As our work shows, NLP is a highly relevant information extraction and processing approach for pharmacology. It has been used extensively, from intelligent searches through thousands of medical documents to finding traces of adversarial drug interactions in social media. We split our coverage into five categories to survey modern NLP methodology, commonly addressed tasks, relevant textual data, knowledge bases, and useful programming libraries. We split each of the five categories into appropriate subcategories, describe their main properties and ideas, and summarize them in a tabular form. The resulting survey presents a comprehensive overview of the area, useful to practitioners and interested observers.