Grabar Natalia, Hamon Thierry
Centre de Recherche des Cordeliers, Université Pierre et Marie Curie - Paris6, UMR S 872, Paris, F-75006; Université Paris Descartes, UMR S 872, Paris, F-75006; INSERM, U872, Paris, F-75006 France.
AMIA Annu Symp Proc. 2009 Nov 14;2009:203-7.
The motivation of this work is to study the use of speculation markers within scientific writing: this may be useful for discovering whether these markers are regularly spread across biomedical articles and then for establishing the logical structure of articles. To achieve these objectives, we compute associations between article sections and speculation markers. We use machine learning algorithms to show that there are strong and interesting associations between speculation markers and article structure. For instance, strong markers, which strongly influence the presentation of knowledge, are specific to Results, Discussion and Abstract; while non strong markers appear with higher regularity within Material and Methods. Our results indicate that speculation is governed by observable usage rules within scientific articles and can help their structuring.
这可能有助于发现这些标记是否在生物医学文章中经常出现，进而有助于建立文章的逻辑结构。为了实现这些目标，我们计算了文章各部分与推测标记之间的关联。我们使用机器学习算法来表明推测标记与文章结构之间存在强烈且有趣的关联。例如，对知识呈现具有强烈影响的强标记特定于结果、讨论和摘要部分；而非强标记在材料与方法部分出现的频率更高。我们的结果表明，在科学文章中，推测受可观察到的使用规则支配，并且有助于文章的结构构建。
