Information Extraction from Public Meeting Articles
Information Extraction from Public Meeting Articles
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DOI：
10.1007/s42979-022-01176-z
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Felix Giovanni Virgo;Koji Tanaka;Kazuki Ashihara;Chenhui Chu;Yuta Nakashima;Noriko Takemura;H. Nagahara;Takao Fujikawa
中科院分区：
文献类型：
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作者：
Felix Giovanni Virgo;Koji Tanaka;Kazuki Ashihara;Chenhui Chu;Yuta Nakashima;Noriko Takemura;H. Nagahara;Takao Fujikawa
公众集会文章是了解澳大利亚公众舆论和公共领域历史的关键。从公开会议文章中提取信息可以获得对澳大利亚历史的新见解。在本文中，我们创建了一个信息提取数据集在公共会议领域。我们手动注释了1258篇公开会议文章的日期和时间、地点、目的、请求会议的人、召集会议的人以及被召集的人。我们进一步提出了一个信息提取系统，它制定了从公共会议的文章作为一个机器阅读理解任务的信息提取。实验结果表明，我们的系统可以达到74.98%的F1分数的信息提取从公共会议的文章。
Public meeting articles are the key to understanding the history of public opinion and public sphere in Australia. Information extraction from public meeting articles can obtain new insights into Australian history. In this paper, we create an information extraction dataset in the public meeting domain. We manually annotate the date and time, place, purpose, people who requested the meeting, people who convened the meeting, and people who were convened of 1258 public meeting articles. We further present an information extraction system, which formulates information extraction from public meeting articles as a machine reading comprehension task. Experiments indicate that our system can achieve an F1 score of 74.98% for information extraction from public meeting articles.