  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.
