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.

本调查研究提供了隐含话语关系识别（IDRR）任务的全面和最新发展情况，包括任务定义、数据源、解决方案及其性能比较，并讨论了该领域的未来研究方向。