More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction

• URL: http://arxiv.org/abs/2004.03186v3
• Date: Wed, 30 Sep 2020 09:15:29 GMT
• Title: More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction
• Authors: Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou
• Abstract summary: People have been working on extracting facts from text for years. With explosion of Web text and emergence of new relations human knowledge is increasing drastically. We show promising directions towards more powerful extraction (RE)
• Score: 106.28192084309617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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