A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/1911.02782v3.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
S2ORC: The Semantic Scholar Open Research Corpus
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<span title="2020-07-07">2020</span>
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arXiv
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<span class="release-stage" >pre-print</span>
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<span title="2020-04-08">2020</span>
<span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1911.02782v2">arXiv:1911.02782v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/svpd3hoehjce7jq23s27l5kbke">fatcat:svpd3hoehjce7jq23s27l5kbke</a> </span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1911.02782v2">arXiv:1911.02782v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/svpd3hoehjce7jq23s27l5kbke">fatcat:svpd3hoehjce7jq23s27l5kbke</a> </span>
<span title="2019-11-07">2019</span>
<span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1911.02782v1">arXiv:1911.02782v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pe5wxjfkmbesxibtjrsl54fmjm">fatcat:pe5wxjfkmbesxibtjrsl54fmjm</a> </span>
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We introduce S2ORC, a large corpus of 81.1M English-language academic papers spanning many academic disciplines. The corpus consists of rich metadata, paper abstracts, resolved bibliographic references, as well as structured full text for 8.1M open access papers. Full text is annotated with automatically-detected inline mentions of citations, figures, and tables, each linked to their corresponding paper objects. In S2ORC, we aggregate papers from hundreds of academic publishers and digital
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... ves into a unified source, and create the largest publicly-available collection of machine-readable academic text to date. We hope this resource will facilitate research and development of tools and tasks for text mining over academic text.
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