A New Corpus to Support Text Mining for the Curation of Metabolites in the ChEBI Database

Research output: Contribution to conferencePaper

  • External authors:
  • Matthew Shardlow
  • Nhung Nguyen
  • Gareth Owen
  • Steve Turner
  • Andrew Leach
  • Claire O'Donovan

Abstract

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.

Bibliographical metadata

Original languageEnglish
Pages280-285
Number of pages6
Publication statusPublished - May 2018
EventProceedings of the Eleventh International Conference on Language Resources and Evaluation - Miyazaki, Japan
Event duration: 7 May 201812 May 2018
Conference number: 11
http://www.lrec-conf.org/proceedings/lrec2018/index.html

Conference

ConferenceProceedings of the Eleventh International Conference on Language Resources and Evaluation
Abbreviated titleLREC 2018
CountryJapan
CityMiyazaki
Period7/05/1812/05/18
Internet address