Home > Research > Publications & Outputs > Infrastructure for Semantic Annotation in the G...

Electronic data

  • genomics

    Accepted author manuscript, 1.06 MB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

  • 2020.lrec-1.855

    Final published version, 1.12 MB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

View graph of relations

Infrastructure for Semantic Annotation in the Genomics Domain

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published
Publication date11/05/2020
Host publicationLREC 2020, Twelfth International Conference on Language Resources and Evaluation: LREC'20
Place of PublicationParis
PublisherEuropean Language Resources Association (ELRA)
Pages6921-6929
Number of pages9
ISBN (print)9791095546344
<mark>Original language</mark>English

Abstract

We describe a novel super-infrastructure for biomedical text mining which incorporates an end-to-end pipeline for the collection, annotation, storage, retrieval and analysis of biomedical and life sciences literature, combining NLP and corpus linguistics methods.
The infrastructure permits extreme-scale research on the open access PubMed Central archive. It combines an updatable Gene Ontology Semantic Tagger (GOST) for entity identification and semantic markup in the literature, with a NLP pipeline scheduler (Buster) to collect and process the corpus, and a bespoke columnar corpus database (LexiDB) for indexing. The corpus database is distributed to permit fast indexing, and provides a simple web front-end with corpus linguistics methods for sub-corpus comparison and retrieval. GOST is
also connected as a service in the Language Application (LAPPS) Grid, in which context it is interoperable with other NLP tools and data in the Grid and can be combined with them in more complex workflows. In a literature based discovery setting, we have created an annotated corpus of 9,776 papers with 5,481,543 words.