Broad-coverage biomedical relation extraction with SemRep.

ABSTRACT:

Background

In the era of information overload, natural language processing (NLP) techniques are increasingly needed to support advanced biomedical information management and discovery applications. In this paper, we present an in-depth description of SemRep, an NLP system that extracts semantic relations from PubMed abstracts using linguistic principles and UMLS domain knowledge. We also evaluate SemRep on two datasets. In one evaluation, we use a manually annotated test collection and perform a comprehensive error analysis. In another evaluation, we assess SemRep's performance on the CDR dataset, a standard benchmark corpus annotated with causal chemical-disease relationships.

Results

A strict evaluation of SemRep on our manually annotated dataset yields 0.55 precision, 0.3

SUBMITTER: Kilicoglu H 

PROVIDER: S-EPMC7222583 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature