This introduction aims to tell the story of how we put words into computers. It is part of the story of the field of natural language processing (NLP), a branch of artificial intelligence. It targets a wide audience with a basic understanding of computer programming, but avoids a detailed mathematical treatment, and it does not present any algorithms. It also does not focus on any particular application of NLP such as translation, question answering, or information extraction. The ideas presented here were developed by many researchers over many decades, so the citations are not exhaustive but rather direct the reader to a handful of papers that are, in the author's view, seminal. After reading this document, you should have a general understanding of word vectors (also known as word embeddings): why they exist, what problems they solve, where they come from, how they have changed over time, and what some of the open questions about them are. Readers already familiar with word vectors are advised to skip to Section 5 for the discussion of the most recent advance, contextual word vectors.
翻译:本导言旨在讲述我们如何将文字输入计算机的故事,这是自然语言处理(NLP)领域的故事的一部分,这是人工智能的一个分支。它针对的对象是对计算机编程有基本理解的广大受众,但避免了详细的数学处理,它没有提出任何算法。它也不侧重于国家语言处理的任何具体应用,例如翻译、答题或信息提取。这里介绍的想法是由许多研究人员几十年来开发的,因此引文不是详尽无遗的,而是引导读者阅读一些文件,作者认为这些文件具有开创性。阅读这份文件后,你应该对文字矢量(也称为“嵌入字”):为什么存在这些源、它们从何而来的问题、它们从何而来、它们是如何变化的,以及它们的某些公开问题是什么。已经熟悉文字矢量的读者们建议跳到第5节,讨论最新的进步、背景文字矢量。