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Acknowledgement Entity Recognition in CORD-19 Papers

机译:CORD-19文件中的确认实体识别

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Acknowledgements are ubiquitous in scholarly papers. Existing acknowledgement entity recognition methods assume all named entities are acknowledged. Here, we examine the nuances between acknowledged and named entities by analyzing sentence structure. We develop an acknowledgement extraction system, ACKExtract based on open-source text mining software and evaluate our method using manually labeled data. AckExtract uses the PDF of a scholarly paper as input and outputs acknowledgement entities. Results show an overall performance of F_1 = 0.92. We built a supplementary database by linking CORD-19 papers with acknowledgement entities extracted by AckExtract including persons and organizations and find that only up to 50-60% of named entities are actually acknowledged. We further analyze chronological trends of acknowledgement entities in CORD-19 papers.
机译:致谢在学术论文中普遍存在。现有确认实体识别方法假设所有命名实体都已确认。在这里,我们通过分析句子结构来检查确认和命名实体之间的细微差别。我们开发了一个确认提取系统,基于开源文本挖掘软件,并使用手动标记数据评估我们的方法。 Ackextract使用学术纸的PDF作为输入和输出确认实体。结果显示F_1 = 0.92的整体性能。我们通过将CORD-19文件与ACKExtract提取的确认实体联系起来,为包括人员和组织提取的确认实体建立了一个补充数据库,并发现最高可达50-60%的命名实体。我们进一步分析了CORD-19文件中确认实体的时间顺序趋势。

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