Abstract
In this article, an end-to-end system was proposed for the challenge task of disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction in BioCreative V, where DNER includes disease mention recognition (DMR) and normalization (DN). Evaluation on the challenge corpus showed that our system achieved the highest F1-scores 86.93% on DMR, 84.11% on DN, 43.04% on CID relation extraction, respectively. The F1-score on DMR is higher than our previous one reported by the challenge organizers (86.76%), the highest F1-score of the challenge.Database URL: http://database.oxfordjournals.org/content/2016/baw077
Highlights
In recent years, chemicals, diseases, and their relations have attracted considerable attention as they play important roles in many areas of biomedical research and healthcare such as biocuration, drug discovery and drug safety surveillance [1]
Evaluation on the corpus of the challenge showed that our system achieved the highest F1-scores of 92.96% on chemical mention recognition (CMR), 86.93% on disease mention recognition (DMR), 92.19% on CN, 84.11% on DN, 43.04% on chemical-induced disease (CID) relation extraction, respectively, higher than the ones reported by the challenge organizers because of post-challenge analysis and improvement
CRFsuite [30], SVMhmm [31] and liblinear [32] were used as implementations of conditional random fields (CRFs), structure support vector machines (SSVMs) and the ensemble meta-classifier for CMR and DMR respectively, SVMrank [33] was used as an implement of support vector machines (SVMs)-rank for CN and DN, and liblinear was used as an implement of the SVM classifier for CID relation extraction
Summary
Chemicals (or drugs), diseases, and their relations have attracted considerable attention as they play important roles in many areas of biomedical research and healthcare such as biocuration, drug discovery and drug safety surveillance [1]. Automatic chemical and disease named entity recognition (DNER) and chemical–disease relation (CDR) extraction remain challenges. Through BioCreative V, a challenge task of automatic extraction of mechanistic and biomarker CDRs from the biomedical literature in support of biocuration, new drug discovery and drug safety surveillance was proposed to advance textmining research on relationship extraction and provide practical benefits to biocuration [2]. This task included two subtasks: DNER, including disease mention
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