We present a new corpus of 200 abstracts and 100 full text papers which have been annotated with named entities and relations in the biomedical domain as part of the OpenMinTeD project. This corpus facilitates the goal in OpenMinTeD of making text and data mining accessible to the users who need it most. We describe the process we took to annotate the corpus with entities (Metabolite, Chemical, Protein, Species, Biological Activity and Spectral Data) and relations (Isolated From, Associated With, Binds With and Metabolite Of). We report inter-annotator agreement (using F-score) for entities of between 0.796 and 0.892 using a strict matching protocol and between 0.875 and 0.963 using a relaxed matching protocol. For relations we report inter annotator agreement of between 0.591 and 0.693 using a strict matching protocol and between 0.744 and 0.793 using a relaxed matching protocol. We describe how this corpus can be used within ChEBI to facilitate text and data mining and how the integration of this work with the OpenMinTeD text and data mining platform will aid curation of ChEBI and other biomedical databases.
@InProceedings{SHARDLOW18.229, author = {Matthew Shardlow and Nhung Nguyen and Gareth Owen and Claire O'Donovan and Andrew Leach and John McNaught and Steve Turner and Sophia Ananiadou}, title = "{A New Corpus to Support Text Mining for the Curation of Metabolites in the ChEBI Database}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }