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CHR Dataset

The CHemical Reactions dataset (CHR) is a distantly supervised dataset dealing with binary interactions between chemicals.

Description

The dataset consists of 12,094 abstracts and their titles from PubMed. The annotation of chemicals was performed using the back-end of the semantic faceted search engine Thalia. Chemical compounds were selected from the annotated entities and aligned with the graph database Biochem4j, a freely available database that integrates several resources such as UniProt, KEGG and the NCBI Taxonomy. If two chemical entities were identified as related in Biochem4j, they were considered as positive instances in the dataset, otherwise as negative. In total, the corpus contains over 100,000 annotated chemicals and 30,000 reactions.

Download

The CHR dataset is available for download. Please observe the terms of the licence if you use the dataset.

Licence

The annotations in the CHR dataset were created at the National Centre for Text Mining (NaCTeM), School of Computer Science, University of Manchester, UK. They are licensed under a Creative Commons Attribution 4.0 International License.

PLEASE ATTRIBUTE NaCTeM WHEN USING THE CORPUS, AND PLEASE CITE THE FOLLOWING ARTICLE:

Sunil K Sahu, Fenia Christopoulou, Makoto Miwa and Sophia Ananiadou. 2019. Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network. In Proceedings of ACL.

References

Sunil K Sahu, Fenia Christopoulou, Makoto Miwa and Sophia Ananiadou. 2019. (In Press). Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network. In Proceedings of ACL.

Axel J Soto, Piotr Przybyła and Sophia Ananiadou. 2018. Thalia: Semantic search engine for biomedical abstracts. Bioinformatics, 35(10): 1799-1801

Neil Swainston, Riza Batista-Navarro, Pablo Carbonell, Paul D Dobson, Mark Dunstan, Adrian J Jervis, Maria Vinaixa, Alan R Williams, Sophia Ananiadou, Jean-Loup Faulon et al. 2017. biochem4j: Integrated and extensible biochemical knowledge through graph databases. PloS ONE, 12(7): e0179130