Can Plausibility Help to Support High Quality Content in Digital Libraries?
Can Plausibility Help to Support High Quality Content in Digital Libraries?
复制标题
DOI：
10.1007/978-3-319-67008-9_14
复制
发表时间：
2017-09
期刊：
影响因子：
--
通讯作者：
J. M. Pinto;Wolf-Tilo Balke
中科院分区：
文献类型：
--
作者：
J. M. Pinto;Wolf-Tilo Balke
本文提出了一种支持数字图书馆高质量内容的新方法，通过引入新科学论文的合理性概念，与先验知识进行对比。特别是，我们的工作提出了一种新的科学论文评估方法来支持审稿人的工作量。所提议的方法关注科学论文的核心组成部分：它的主张。我们的方法利用了从PubMed抓取的科学论文数字图书馆的文本和主题建模的最先进的神经嵌入表示。作为似是而非的概念的潜在有用性的证明，我们研究和报告实验与声明表示为统计关联的文件。这种类型的声明经常出现在医学、化学、生物、营养等领域，其中一种药物、物质、产品等的消费对其他类型的实体（如疾病、另一种药物、物质等）有影响。
Presented herein is a novel approach to support high quality content in Digital Libraries by introducing the notion ofPlausibilityof new scientific papers when contrasted with prior knowledge. In particular, our work proposes a novel assessment of scientific papers to support the workload of reviewers. The proposed approach focus on a core component of a scientific paper: its claim. Our methodology exploits state of the art neural embedding representation of text and topic modeling on a Digital Library of scientific papers crawled from PubMed. As a proof of concept of the potential usefulness of the notion of Plausibility, we study and report experiments on documents with claims expressed as statistical associations. This type of claims is very often found in medicine, chemistry, biology, nutrition, etc. where the consumption of a drug, substance, product, etc., has an effect on some other type of entity such as a disease, another drug, substance, etc.