Complex Relation Extraction: Challenges and Opportunities

• URL: http://arxiv.org/abs/2012.04821v1
• Date: Wed, 9 Dec 2020 02:05:00 GMT
• Title: Complex Relation Extraction: Challenges and Opportunities
• Authors: Haiyun Jiang, Qiaoben Bao, Qiao Cheng, Deqing Yang, Li Wang and Yanghua Xiao
• Abstract summary: Relation extraction aims to identify the target relations of entities in texts. Traditional binary relation extraction, including supervised, semi-supervised and distant supervised ones, has been extensively studied. In recent years, many complex relation extraction tasks are proposed to meet the complex applications in practice.
• Score: 20.88725215959468
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Relation extraction aims to identify the target relations of entities in texts. Relation extraction is very important for knowledge base construction and text understanding. Traditional binary relation extraction, including supervised, semi-supervised and distant supervised ones, has been extensively studied and significant results are achieved. In recent years, many complex relation extraction tasks, i.e., the variants of simple binary relation extraction, are proposed to meet the complex applications in practice. However, there is no literature to fully investigate and summarize these complex relation extraction works so far. In this paper, we first report the recent progress in traditional simple binary relation extraction. Then we summarize the existing complex relation extraction tasks and present the definition, recent progress, challenges and opportunities for each task.

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