title: Machine learning-based detection of chemical risk.
abstract: Chemical risk appears with chemical substances that are dangerous for human or animal health, or for environment, such as with Bisphenol A and phtalates. Chemical risk causes several severe health disorders and is particularly dangerous for human health. Specific agencies are involved in the verification of the suitability of products and goods to be marketed. For this, a large amount of scientific and institutional literature is manually analyzed to study the current knowledge on the associated chemical risk. We propose to use machine learning and dedicated classification for the automatic detection of chemical risk statements. We test several algorithms and features and obtain between 0.60 and 0.95 F-measure.
authors.affiliation: CNRS UMR 8163 STL, Université Lille 3, 59653 Villeneuve d'Ascq, France.
authors.forename: Natalia
authors.initials: N
authors.lastname: Grabar
authors.affiliation: CNRS UMR 8163 STL, Université Lille 3, 59653 Villeneuve d'Ascq, France.
authors.forename: Ornella
authors.initials: O
authors.lastname: Wandji Tchami
authors.affiliation: Institut des Sciences de la Communication, CNRS UPS 3088, France.
authors.forename: Laura
authors.initials: L
authors.lastname: Maxim
mesh_terms: D001185:Artificial Intelligence; D001559:Benzhydryl Compounds / Q000506:poisoning*; D052244:Endocrine Disruptors / Q000633:toxicity*; D009323:Natural Language Processing; D010506:Periodicals as Topic / Q000145:classification*; D010636:Phenols / Q000506:poisoning*; D018570:Risk Assessment / Q000379:methods*; D000098826:Bisphenol A Compounds
chemical_list: D001559:Benzhydryl Compounds; D052244:Endocrine Disruptors; D010636:Phenols; C006780:bisphenol A; D000098826:Bisphenol A Compounds