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The advancement of biomedical named entity recognition (BNER) and biomedicalrelation extraction (BRE) researches promotes the development of text mining inbiological domains. As a cornerstone of BRE, robust BNER system is required toidentify the mentioned NEs in plain texts for further relation extractionstage. However, the current BNER corpora, which play important roles in thesetasks, paid less attention to achieve the criteria for BRE task. In this study,we present Revised JNLPBA corpus, the revision of JNLPBA corpus, to broaden theapplicability of a NER corpus from BNER to BRE task. We preserve the originalentity types including protein, DNA, RNA, cell line and cell type while all theabstracts in JNLPBA corpus are manually curated by domain experts again basison the new annotation guideline focusing on the specific NEs instead of generalterms. Simultaneously, several imperfection issues in JNLPBA are pointed outand made up in the new corpus. To compare the adaptability of different NERsystems in Revised JNLPBA and JNLPBA corpora, the F1-measure was measured inthree open sources NER systems including BANNER, Gimli and NERSuite. In thesame circumstance, all the systems perform average 10% better in Revised JNLPBAthan in JNLPBA. Moreover, the cross-validation test is carried out which wetrain the NER systems on JNLPBA/Revised JNLPBA corpora and access theperformance in both protein-protein interaction extraction (PPIE) andbiomedical event extraction (BEE) corpora to confirm that the newly refinedRevised JNLPBA is a competent NER corpus in biomedical relation application.The revised JNLPBA corpus is freely available atiasl-btm.iis.sinica.edu.tw/BNER/Content/Revised_JNLPBA.zip.

Revised JNLPBA Corpus: A Revised Version of Biomedical NER Corpus for Relation Extraction Task