论文标题
REL：站在巨人肩上的实体链接器
REL: An Entity Linker Standing on the Shoulders of Giants
论文作者
论文摘要
实体链接是现代检索系统中通常由第三方工具包执行的标准组件。尽管有大量的开源选项，但很难找到一个具有模块化体系结构的系统，该系统可以更换某些组件，不依赖于外部来源，很容易更新为较新的Wikipedia版本，并且最重要的是，最重要的是具有最先进的性能。本文介绍的REL系统旨在填补这一空白。基于自然语言处理研究的最新神经组件，它作为Python软件包以及Web API提供。我们还报告了针对良好的系统和链接基准的标准实体的当前最先进的实验比较。
Entity linking is a standard component in modern retrieval system that is often performed by third-party toolkits. Despite the plethora of open source options, it is difficult to find a single system that has a modular architecture where certain components may be replaced, does not depend on external sources, can easily be updated to newer Wikipedia versions, and, most important of all, has state-of-the-art performance. The REL system presented in this paper aims to fill that gap. Building on state-of-the-art neural components from natural language processing research, it is provided as a Python package as well as a web API. We also report on an experimental comparison against both well-established systems and the current state-of-the-art on standard entity linking benchmarks.
