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2 results for “in-text citation”
A guiding diagram for the selection of a CiTO citation function for a given in-text citation
<p>A decision model for the selection of a Citation Typing Ontology (CiTO, <a href="http://purl.org/spar/cito">http://purl.org/spar/cito</a>) citation function to use for the annotation of the citation intent of an examined in-text citation based on its context. The first large row of the diagram contains three macro-categories: (1) “Reviewing …”, (2) “Affecting …”, and (3) “Referring …”. Each macro-category has at least two subcategories, and each subcategory refers to a set of citation functions. The first row defines the suitable citation functions for it with the help of a guiding sentence to be completed according to the chosen sub-category and citation function. </p> <p>To annotate the in-text citation intent using the guiding scheme (see Figure), we use a priority ranked strategy that works as follows: </p> <ol> <li>we match each in-text citation against at least one of the three macro-categories, i.e. “Reviewing ...”, “Affecting ...” and “Referring ...” (first row in Figure);</li> <li>for each macro-category we have selected in (1), we choose one or more citation functions from those provided by CiTO;</li> <li>in case we select only one citation function, then we annotate the in-text citation intent with such a value; otherwise</li> <li>we calculate the priority of each citation function we have selected by summing its value in parenthesis (from 0.1 to 0.6) with the corresponding value in the y-axis (from 10 to 50) and in the x-axis (from 1 to 8) shown in Figure. The smaller the sum, the more priority the citation function has. For instance, the priority of the citation function “confirms” is 11.2 that is higher than the one of the citation function “describes”, which is 43.2. Finally, we select the citation function that has higher priority and annotate the in-text citation function with it.</li> </ol>
unarXive: A Large Scholarly Data Set with Publications' Full-Text, Annotated In-Text Citations, and Links to Metadata
<h2><strong>Description</strong></h2> <p><strong>unarXive</strong> is a scholarly data set containing <strong>publications' full-text</strong>, annotated <strong>in-text citations</strong>, and a <strong>citation network</strong>.</p> <p>The data is <strong>generated from all LaTeX sources on </strong><a href="https://arxiv.org/"><strong>arXiv</strong></a> and therefore of higher quality than data generated from PDF files.</p> <p>Typical <strong>use cases</strong> are</p> <ul> <li>Citation recommendation</li> <li>Citation context analysis</li> <li>Bibliographic analyses</li> <li>Reference string parsing</li> </ul> <p>This version (v3) of our data set is based on all arXiv publications until 2020-07-31 and on the Microsoft Academic Graph as of 2020-08-18. As additional contribution, we included a table with the publication date and the scientific discipline for each paper for easier filtering.</p> <p><strong>Note:</strong> This Zenodo record is an old version of unarXive. You can find the <strong>most recent version</strong> at <a href="../record/7752754">https://zenodo.org/record/7752754</a> and <a href="../record/7752615">https://zenodo.org/record/7752615</a></p> <h2><strong>Access</strong></h2> <p>┏━━━━━━━━━━━━━━━━━━━━━━━━━━┓<br>┃ <a href="https://github.com/IllDepence/unarXive/blob/legacy_2020/doc/unarXive_sample.tar.bz2"><strong>D O W N L O A D S A M P L E</strong></a>  ┃<br>┗━━━━━━━━━━━━━━━━━━━━━━━━━━┛</p> <p>To download the whole data set send an access request and note the following:</p> <blockquote> <p><strong>Note</strong>: this Zenodo record is a "full" version of unarXive, which was generated from all of arXiv.org <em>including non-permissively licensed papers</em>. Make sure that your use of the data is compliant with the paper's licensing terms.¹</p> <p>¹ For information on papers' licenses use <a href="https://info.arxiv.org/help/bulk_data/index.html">arXiv's bulk metadata access</a>.</p> </blockquote> <p>The <strong>code</strong> used for generating the data set is <a href="https://github.com/IllDepence/unarXive/tree/legacy_2020/">publicly available</a>.</p> <p><strong>Usage examples</strong> for our data set are provided at <a href="https://github.com/IllDepence/unarXive/tree/legacy_2020/#usage-examples">here on GitHub</a>.</p> <h2><strong>Citing</strong></h2> <p>This initial version of unarXive is described in the following journal article.</p> <p><em>Tarek Saier, Michael Färber: "</em><a href="http://dx.doi.org/10.1007/s11192-020-03382-z"><em>unarXive: A Large Scholarly Data Set with Publications' Full-Text, Annotated In-Text Citations, and Links to Metadata</em></a><em>", Scientometrics, 2020,</em><br>[<a href="https://www.aifb.kit.edu/images/f/f9/UnarXive_Scientometrics2020.pdf">link to an author copy]</a></p> <p>The <strong>updated version</strong> is described in the following conference paper.</p> <p><em>Tarek Saier, Michael Färber. "</em><a href="10.1109/JCDL57899.2023.00020"><em>unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network</em></a><em>", JCDL 2023.</em><br>[<a href="https://doi.org/10.48550/arXiv.2303.14957">link to an author copy</a>]</p>
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