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On the Contribution of Specific Entity Detection in Comparative Constructions to Automatic Spin Detection in Biomedical Scientific Publications

机译：关于比较结构中特定实体检测对生物医学科学出版物自动旋转检测的贡献

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摘要

In this article, we address the problem of providing automated aid for the detection of misrepresentation ("spin") of research results in scientific publications from the biomedical domain. Our goal is to identify automatically inadequate claims in medical articles, i.e. claims that present the beneficial effect of the experimental treatment to be greater than it is actually proven by the research results. To this end, we propose a Natural Language Processing (NLP) approach. We first make a review of related work and an NLP analysis of the problem; then we present our first results obtained on the articles that report results of Randomized Controlled Trials (RCTs), i.e. clinical trials comparing two or more interventions by randomly assigning them to patients. Our first experiments concern the identification of entities specific to RCTs (outcomes and patient groups), obtained with basic methods (local grammars) on a corpus extracted from the PubMed open archive. We explore the possibility to extract outcomes from comparative constructions that are commonly used to report results of clinical trials. Our second set of experiments consists in extracting outcomes from a manually annotated corpus using deep learning methods.
机译：在本文中，我们解决了从生物医学领域的科学出版物的研究结果提供了自动化辅助的问题，从而提供了对研究结果的自动化援助。我们的目标是在医学制品中识别自动不足，即，提出实验治疗的有益效果大于其实际研究结果实际证明。为此，我们提出了一种自然语言处理（NLP）方法。我们首先对问题进行了审查和NLP分析;然后我们介绍我们在报告随机对照试验（RCT）的结果的文章中获得的第一个结果，即通过随机将其分配给患者进行两种或更多干预措施的临床试验。我们的第一个实验涉及鉴定对RCT（结果和患者群体）的实体的鉴定，并在从PubMed Open Archive中提取的语料库上获得了基本方法（局部语法）。我们探讨了从常用于报告临床试验结果的比较结构中提取结果的可能性。我们的第二组实验包括使用深层学习方法从手动注释的语料库中提取结果。

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