title: A knowledge-poor approach to chemical-disease relation extraction.
abstract: The article describes a knowledge-poor approach to the task of extracting Chemical-Disease Relations from PubMed abstracts. A first version of the approach was applied during the participation in the BioCreative V track 3, both in Disease Named Entity Recognition and Normalization (DNER) and in Chemical-induced diseases (CID) relation extraction. For both tasks, we have adopted a general-purpose approach based on machine learning techniques integrated with a limited number of domain-specific knowledge resources and using freely available tools for preprocessing data. Crucially, the system only uses the data sets provided by the organizers. The aim is to design an easily portable approach with a limited need of domain-specific knowledge resources. In the participation in the BioCreative V task, we ranked 5 out of 16 in DNER, and 7 out of 18 in CID. In this article, we present our follow-up study in particular on CID by performing further experiments, extending our approach and improving the performance.
authors.affiliation: Department of Information Engineering and Computer Science, University of Trento, Italy.
authors.forename: Firoj
authors.initials: F
authors.lastname: Alam
authors.affiliation: Department of Electrical Engineering and Information Technologies, University of Napoli Federico II, Napoli, Italy.
authors.forename: Anna
authors.initials: A
authors.lastname: Corazza
authors.affiliation: Center for Information and Communication Technology Fondazione Bruno Kessler, Trento, Italy lavelli@fbk.eu.
authors.forename: Alberto
authors.initials: A
authors.lastname: Lavelli
authors.affiliation: Center for Information and Communication Technology Fondazione Bruno Kessler, Trento, Italy.
authors.forename: Roberto
authors.initials: R
authors.lastname: Zanoli
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D004194:Disease; D006801:Humans; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods*; D014116:Toxicology / Q000379:methods*