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    PubRunner: A light-weight framework for updating text mining results [version 2]

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    Author
    Busby, B.
    Anekalla, K.R.
    Courneya, J.P.
    Date
    2017
    Journal
    F1000Research
    Publisher
    Faculty of 1000 Ltd
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.12688/f1000research.11389.2
    Abstract
    Biomedical text mining promises to assist biologists in quickly navigating the combined knowledge in their domain. This would allow improved understanding of the complex interactions within biological systems and faster hypothesis generation. New biomedical research articles are published daily and text mining tools are only as good as the corpus from which they work. Many text mining tools are underused because their results are static and do not reflect the constantly expanding knowledge in the field. In order for biomedical text mining to become an indispensable tool used by researchers, this problem must be addressed. To this end, we present PubRunner, a framework for regularly running text mining tools on the latest publications. PubRunner is lightweight, simple to use, and can be integrated with an existing text mining tool. The workflow involves downloading the latest abstracts from PubMed, executing a user-defined tool, pushing the resulting data to a public FTP or Zenodo dataset, and publicizing the location of these results on the public PubRunner website. We illustrate the use of this tool by re-running the commonly used word2vec tool on the latest PubMed abstracts to generate up-to-date word vector representations for the biomedical domain. This shows a proof of concept that we hope will encourage text mining developers to build tools that truly will aid biologists in exploring the latest publications. Copyright 2017 Anekalla KR et al.
    Keyword
    Biomedical text mining
    BioNLP
    Natural language processing
    PubMed
    PubRunner
    Text mining
    Identifier to cite or link to this item
    https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033684357&doi=10.12688%2ff1000research.11389.2&partnerID=40&md5=18b4e023072955f17f9038fb6dd779c6; http://hdl.handle.net/10713/11365
    ae974a485f413a2113503eed53cd6c53
    10.12688/f1000research.11389.2
    Scopus Count
    Collections
    UMB Open Access Articles 2017

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