1164161
doi
10.5281/zenodo.1164161
oai:zenodo.org:1164161
user-miror
user-eu
Paroubek Patrick
LIMSI-CNRS
Automatic detection of inadequate claims in biomedical articles: first steps
Koroleva Anna
LIMSI-CNRS
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Inadequate Reporting
Spin
Biomedical Articles
Text Classification
Entity Extraction
<p>In this article we present the first steps in developing an NLP algorithm for automatic detection of inadequate reporting of research results (known as spin) in biomedical articles. Inadequate reporting consists in presenting the experimental treatment as having a greater beneficial effect than it was shown by the research results. We propose a scheme for an algorithm that would automatically identify important claims in the articles abstracts, extract possible<br>
supporting information from the article and check the adequacy of the claims. We present the state of the art and our first experiments for three tasks related to spin detection: classification of articles according to the type of reported clinical trial; classification of sentences in the abstracts aimed at identifying mentions of the Results and Conclusions of the experiment; and extraction of some trial characteristics. For each task, we outline possible directions of further work.</p>
Zenodo
2017-09-12
info:eu-repo/semantics/conferencePaper
1164160
user-miror
user-eu
award_title=Methods in Research on Research; award_number=676207; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/676207; funder_id=00k4n6c32; funder_name=European Commission;
1579541429.363001
236588
md5:fee735171a73a2adc2cb7dbafdc3ff86
https://zenodo.org/records/1164161/files/MEDA-2017_paper_4.pdf
public
10.5281/zenodo.1164160
isVersionOf
doi