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(NLP)是人工智能的一个领域,它应用信息技术处理人类语言,在某种程度上理解它,并在各种应用中加以使用。这个领域在过去几年中迅速发展,现在利用深神经网络的现代变体从大文本体中提取相关模式。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。正如我们的工作所显示的,NLP是药理学高度相关的信息提取和处理方法。从智能搜索到数千份医疗文件,到在社交媒体中找到对抗性药物互动的痕迹,已经广泛使用。我们把覆盖分为五个类别,调查现代NLP方法,共同处理的任务、相关的文本数据、知识基础和有用的编程图书馆。我们把这五个类别中的每一个类别分成适当的子类,说明它们的主要性质和想法,并以表格形式加以归纳。由此产生的调查对实践者和感兴趣的观察者都有用。