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CoCoScore: Context-aware co-occurrence scoring for text mining applications using distant supervision

View ORCID ProfileAlexander Junge, Lars Juhl Jensen
doi: https://doi.org/10.1101/444398
Alexander Junge
Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen N, Denmark
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Lars Juhl Jensen
Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen N, Denmark
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Abstract

Information extraction by mining the scientific literature is key to uncovering relations between biomedical entities. Most existing approaches based on natural language processing extract relations from single sentence-level co-mentions, ignoring co-occurrence statistics over the whole corpus. Existing approaches counting entity co-occurrences ignore the textual context of each co-occurrence. We propose a novel corpus-wide co-occurrence scoring approach to relation extraction that takes the textual context of each co-mention into account. Our method, called CoCoScore, scores the certainty of stating an association for each sentence that co-mentions two entities. CoCoScore is trained using distant supervision based on a gold-standard set of associations between entities of interest. Instead of requiring a manually annotated training corpus, co-mentions are labeled as positives/negatives according to their presence/absence in the gold standard. We show that CoCoScore outperforms previous approaches in identifying human disease–gene and tissue–gene associations as well as in identifying physical and functional protein–protein associations in different species. CoCoScore is a versatile text-mining tool to uncover pairwise associations via co-occurrence mining, within and beyond biomedical applications. CoCoScore is available at: https://github.com/JungeAlexander/cocoscore

Footnotes

  • Contact: alexander.junge{at}cpr.ku.dk and lars.juhl.jensen{at}cpr.ku.dk

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 16, 2018.
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CoCoScore: Context-aware co-occurrence scoring for text mining applications using distant supervision
Alexander Junge, Lars Juhl Jensen
bioRxiv 444398; doi: https://doi.org/10.1101/444398
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CoCoScore: Context-aware co-occurrence scoring for text mining applications using distant supervision
Alexander Junge, Lars Juhl Jensen
bioRxiv 444398; doi: https://doi.org/10.1101/444398

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