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.

翻译：自然语言处理是人工智能的一个分支，它运用信息技术处理人类语言，实现一定程度上的理解，并将其应用于各种场景。近年来该领域发展迅速，目前采用深度神经网络的现代变体从大规模文本语料库中提取相关模式。本工作的主要目标是综述自然语言处理在药理学领域的最新应用。我们的研究表明，NLP是一种高度相关的药理学信息提取与处理方法，其应用范围极其广泛——从对数千份医学文献的智能检索，到在社交媒体中识别药物不良相互作用证据。我们将综述内容划分为五个类别，分别涵盖现代NLP方法论、常见处理任务、相关文本数据、知识库及实用编程库，并对每个类别进一步细分，描述其主要特性与思路，最终以表格形式进行归纳总结。本综述为该领域提供了全面的概述，对从业者和相关研究者均具有参考价值。