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 past 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.

中文翻译：

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药理学自然语言处理综述

自然语言处理（Natural Language Processing，NLP）是人工智能的一个领域，它应用信息技术对人类语言进行处理，在一定程度上理解人类语言，并将其用于各种应用。该领域在过去几年迅速发展，现在使用深度神经网络的现代变体从大型文本语料库中提取相关模式。这项工作的主要目的是调查最近在药理学领域使用 NLP 的情况。正如我们的工作所示，NLP 是一种高度相关的药理学信息提取和处理方法。它已被广泛使用，从对数千份医疗文件进行智能搜索，到在社交媒体中寻找对抗性药物相互作用的痕迹。我们将覆盖范围分为五类来调查现代 NLP：方法论、常见任务、相关的文本数据、知识库和有用的编程库。我们将这五个类别中的每一个都分成适当的子类别，描述它们的主要特性和思想，并以表格形式对其进行总结。由此产生的调查提供了对该领域的全面概述，对从业者和感兴趣的观察者很有用。