Entity Linking (EL) empowers Natural Language Processing applications by linking relevant mentions found in raw textual data to precise information about what they supposedly stand for. However, EL approaches have mostly focused on particular kinds of inputs and frequently fail to properly handle texts from specific sources (e.g., microblogs) that have particularities such as grammatical errors, slangs, lack of contextual information and other problems, besides difficulties to exploit their associated data (e.g., time stamps, geographic indicators, authors’ profile data). Some EL approaches have been devised to circumvent such challenges. They exploit several inputs, data features, and EL methods in a synergetic process for more powerful and robust collective EL. This paper reviews recent works that employ such holistic strategies for EL, discusses their limitations, and proposes directions for further advancing holistic EL approaches.

实体链接（EL）通过将原始文本数据中找到的相关提及链接到有关它们据称代表的精确信息，来增强自然语言处理应用程序的功能。但是，EL方法主要集中在特定种类的输入上，并且经常无法正确处理来自特定来源（例如微博）的文本，这些文本具有诸如语法错误，语，缺少上下文信息和其他问题等特殊性，除了难以利用它们相关数据（例如，时间戳，地理标志，作者的个人资料数据）。已经设计出一些EL方法来克服这些挑战。他们在协同过程中利用了几种输入，数据功能和EL方法，从而获得了更强大，更强大的集体EL。本文回顾了最近针对EL采用这种整体策略的作品，