Exploitation of speculation markers to identify the structure of biomedical scientific writing

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.