JEL：一种将知识图谱实体与新闻提及相连接的新型模型
JEL: A Novel Model Linking Knowledge Graph entities to News Mentions
AI总结：
本文提出JEL，一种计算高效的多神经网络端到端实体链接模型，超越现有最先进方法，旨在将知识图谱实体与新闻提及准确连接，支撑新闻分析等应用。
摘要：
我们提出了JEL，一种新颖的、计算高效的多神经网络端到端实体链接模型，它超越了当前最先进的模型。知识图谱已成为一种引人注目的抽象，用于捕获感兴趣实体之间的关键关系，并整合来自多个异构来源的数据。利用知识图谱的一个核心问题是将其实体正确链接到文本来源（如新闻、博客等）中遇到的提及（例如，人物、公司名称），因为每个提及需要考虑数千个实体。这种将提及与实体相连接的任务被称为实体链接（EL）。它是自然语言处理中的一项基本任务，并在各种用例中大有裨益，例如构建新闻分析平台。在摩根大通，新闻分析是一项重要任务，惠及公司内部的多个团队。根据创新数字团队进行的一项调查，公司内约有25个团队正在积极寻求新闻分析解决方案，且每年在外部供应商成本上花费超过200万美元。实体链接对于将非结构化新闻文本与知识图谱连接起来至关重要，使用户能够访问知识图谱中大量经过整理的数据，并极大地促进他们的日常工作。
英文摘要：
We present JEL, a novel computationally efficient end-to-end multi-neural network based entity linking model, which beats current state-of-art model. Knowledge Graphs have emerged as a compelling abstraction for capturing critical relationships among the entities of interest and integrating data from multiple heterogeneous sources. A core problem in leveraging a knowledge graph is linking its entities to the mentions (e.g., people, company names) that are encountered in textual sources (e.g., news, blogs., etc) correctly, since there are thousands of entities to consider for each mention. This task of linking mentions and entities is referred as Entity Linking (EL). It is a fundamental task in natural language processing and is beneficial in various uses cases, such as building a New Analytics platform. News Analytics, in JPMorgan, is an essential task that benefits multiple groups across the firm. According to a survey conducted by the Innovation Digital team 1 , around 25 teams across the firm are actively looking for news analytics solutions, and more than \$2 million is being spent annually on external vendor costs. Entity linking is critical for bridging unstructured news text with knowledge graphs, enabling users access to vast amounts of curated data in a knowledge graph and dramatically facilitating their daily work.
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