CCKS 2019 Shared Task on Inter-Personal Relationship Extraction
Haitao Wang, Zhengqiu He, Tong Zhu, Hao Shao, Wenliang Chen, Min Zhang

TL;DR
This paper describes the CCKS 2019 shared task focused on extracting relationships between pairs of persons in text, detailing the task setup, data, evaluation, and summarizing team approaches and results.
Contribution
It introduces a new shared task on inter-personal relationship extraction, providing a benchmark dataset and evaluation framework for the research community.
Findings
358 teams participated, showcasing diverse approaches.
Evaluation results highlight the effectiveness of different methods.
The task advances research in relationship extraction from text.
Abstract
The CCKS2019 shared task was devoted to inter-personal relationship extraction. Given two person entities and at least one sentence containing these two entities, participating teams are asked to predict the relationship between the entities according to a given relation list. This year, 358 teams from various universities and organizations participated in this task. In this paper, we present the task definition, the description of data and the evaluation methodology used during this shared task. We also present a brief overview of the various methods adopted by the participating teams. Finally, we present the evaluation results.
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Taxonomy
TopicsTopic Modeling · Data Quality and Management · Mental Health via Writing
