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.
Highlights
Manual curation of chemical-disease relations (CDRs) from the literature is expensive and it is difficult to keep up with the growing amount of relevant literature
In the spirit of better matching the actual requirements of practical applications, we decided to approach the tasks in the CDRs track at BioCreative-V, which are different in a few respects from the usual named entity recognition (NER) and relation extraction (RE) tasks
In the first version of the system that participated in the BioCreative V CDR task [16], we only considered a Document Level Classifier (DLC), which takes such a Feature Vector (FV) built from the abstract for every pair of chemical and disease entities
Summary
Manual curation of chemical-disease relations (CDRs) from the literature is expensive and it is difficult to keep up with the growing amount of relevant literature. Comparative toxicogenomics database As a domain-specific resource we have exploited the CTD [20], a publicly available database that aims to advance understanding about how environmental exposures to chemicals affect human health It provides manually curated information about chemicals, and diseases that, in our approach, are used to capture the different ways the entities are mentioned in texts. It is worth mentioning another problem that often comes up with Named Entity Recognition in biomedical texts, and that requires to identify and resolve composite named entities, where a single span refers to more than one concept (e.g. neurological and cardiovascular toxicity) In this regard, only 1% of disease and chemical mentions are composite mentions in the provided data set, and so we do not use any specific resource (e.g. SimConcept tool) to deal with such cases. We consider four binary relation features, depending on both entities, defined as follows: 1. Is the entity pair listed as a positive chemical-disease relation in the CTD [20]?
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