A discourse containing one or more sentences describes daily issues and events for people to communicate their thoughts and opinions. As sentences are normally consist of multiple text segments, correct understanding of the theme of a discourse should take into consideration of the relations in between text segments. Although sometimes a connective exists in raw texts for conveying relations, it is more often the cases that no connective exists in between two text segments but some implicit relation does exist in between them. The task of implicit discourse relation recognition (IDRR) is to detect implicit relation and classify its sense between two text segments without a connective. Indeed, the IDRR task is important to diverse downstream natural language processing tasks, such as text summarization, machine translation and so on. This article provides a comprehensive and up-to-date survey for the IDRR task. We first summarize the task definition and data sources widely used in the field. We categorize the main solution approaches for the IDRR task from the viewpoint of its development history. In each solution category, we present and analyze the most representative methods, including their origins, ideas, strengths and weaknesses. We also present performance comparisons for those solutions experimented on a public corpus with standard data processing procedures. Finally, we discuss future research directions for discourse relation analysis.

包含一个或多个句子的话语描述日常问题和事件，供人们交流思想和观点。由于句子通常由多个文本片段组成，正确理解一篇文章的主题应该考虑文本片段之间的关系。尽管有时原始文本中存在连接词以传达关系，但更常见的情况是两个文本片段之间不存在连接词，但它们之间确实存在某种隐含关系。隐式语篇关系识别（IDRR）的任务是检测隐式关系并在没有连接词的两个文本片段之间对其意义进行分类。事实上，IDRR 任务对于多样化的下游自然语言处理很重要文本摘要、机器翻译等任务。本文为 IDRR 任务提供了全面且最新的调查。我们首先总结了该领域广泛使用的任务定义和数据源。我们从其发展历史的角度对 IDRR 任务的主要解决方法进行了分类。在每个解决方案类别中，我们都会介绍和分析最具代表性的方法，包括它们的起源、想法、优点和缺点。我们还对那些在公共语料库上使用标准数据处理程序进行实验的解决方案进行了性能比较。最后，我们讨论了话语关系分析的未来研究方向。