论文标题
更多数据，更多的关系，更多的上下文和更多的开放性：审查和提取关系的前景
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction
论文作者
论文摘要
关系事实是人类知识的重要组成部分，它们被大量文本隐藏。为了从文本中提取这些事实，人们多年来一直在研究关系提取（RE）。从早期的模式匹配到当前的神经网络，现有的RE方法取得了重大进展。然而，随着Web文本的爆炸和新关系的出现，人类知识正在大幅增长，因此我们需要从RE：更强大的RE系统中“更多”，它可以强大地利用更多数据，有效地学习更多的关系，轻松地处理更复杂的环境，并灵活地推广到更开放的领域。在本文中，我们回顾了现有的RE方法，分析当今面临的关键挑战，并向更强大的RE展示了有希望的方向。我们希望我们的观点能够推进这一领域，并激发社区中的更多努力。
Relational facts are an important component of human knowledge, which are hidden in vast amounts of text. In order to extract these facts from text, people have been working on relation extraction (RE) for years. From early pattern matching to current neural networks, existing RE methods have achieved significant progress. Yet with explosion of Web text and emergence of new relations, human knowledge is increasing drastically, and we thus require "more" from RE: a more powerful RE system that can robustly utilize more data, efficiently learn more relations, easily handle more complicated context, and flexibly generalize to more open domains. In this paper, we look back at existing RE methods, analyze key challenges we are facing nowadays, and show promising directions towards more powerful RE. We hope our view can advance this field and inspire more efforts in the community.
