  Computational chemistry develops fast in recent years due to the rapid growth
and breakthroughs in AI. Thanks for the progress in natural language
processing, researchers can extract more fine-grained knowledge in publications
to stimulate the development in computational chemistry. While the works and
corpora in chemical entity extraction have been restricted in the biomedicine
or life science field instead of the chemistry field, we build a new corpus in
chemical bond field annotated for 7 types of entities: compound, solvent,
method, bond, reaction, pKa and pKa value. This paper presents a novel BERT-CRF
model to build scientific chemical data chains by extracting 7 chemical
entities and relations from publications. And we propose a joint model to
extract the entities and relations simultaneously. Experimental results on our
Chemical Special Corpus demonstrate that we achieve state-of-art and
competitive NER performance.
