  The advancement of biomedical named entity recognition (BNER) and biomedical
relation extraction (BRE) researches promotes the development of text mining in
biological domains. As a cornerstone of BRE, robust BNER system is required to
identify the mentioned NEs in plain texts for further relation extraction
stage. However, the current BNER corpora, which play important roles in these
tasks, 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 the
applicability of a NER corpus from BNER to BRE task. We preserve the original
entity types including protein, DNA, RNA, cell line and cell type while all the
abstracts in JNLPBA corpus are manually curated by domain experts again basis
on the new annotation guideline focusing on the specific NEs instead of general
terms. Simultaneously, several imperfection issues in JNLPBA are pointed out
and made up in the new corpus. To compare the adaptability of different NER
systems in Revised JNLPBA and JNLPBA corpora, the F1-measure was measured in
three open sources NER systems including BANNER, Gimli and NERSuite. In the
same circumstance, all the systems perform average 10% better in Revised JNLPBA
than in JNLPBA. Moreover, the cross-validation test is carried out which we
train the NER systems on JNLPBA/Revised JNLPBA corpora and access the
performance in both protein-protein interaction extraction (PPIE) and
biomedical event extraction (BEE) corpora to confirm that the newly refined
Revised JNLPBA is a competent NER corpus in biomedical relation application.
The revised JNLPBA corpus is freely available at
iasl-btm.iis.sinica.edu.tw/BNER/Content/Revised_JNLPBA.zip.
