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89 results for “authorship”
PAN19 Authorship Analysis: Style Change Detection
<p>This is the data set for the <a href="http://pan.webis.de/clef19/pan19-web/style-change-detection.html">Style Change Detection task</a> of <a href="http://pan.webis.de/clef19/pan19-web/">PAN@CLEF 2019</a>.</p> <p>The goal of the style change detection task is to identify text positions within a given multi-author document at which the author switches. Detecting these positions is a crucial part of the authorship identification process, and for multi-author document analysis in general. Note that, for this task, we make the assumption that a change in writing style always signifies a change in author.</p> <p><strong>Tasks</strong></p> <p>Given a document, we ask participants to answer the following two questions:</p> <ul> <li>Was the given document written by multiple authors? (task 1)</li> <li>For each pair of consecutive paragraphs in the given document: is there a style change between these paragraphs? (task 2)</li> </ul> <p>In other words, the goal is to determine whether the given document contains style changes and if it indeed does, we aim to find the position of the change in the document (between paragraphs).</p> <p>All documents are provided in English and may contain zero up to ten style changes, resulting from at most three different authors. However, style changes may only occur between paragraphs (i.e., a single paragraph is always authored by a single author and does not contain any style changes).</p> <p><strong>Data</strong></p> <p>To develop and then test your algorithms, two data sets including ground truth information are provided. Those data sets differ in their topical breadth (i.e., the number of different topics that are covered in the documents contained). dataset-narrow contains texts from a relatively narrow set of subjects matters (all related to technology), whereas dataset-wide adds additional subject areas to that (travel, philosophy, economics, history, etc.).</p> <p>Both of those data sets are split into three parts:</p> <ul> <li><em>training set:</em> Contains 50% of the whole data set and includes ground truth data. Use this set to develop and train your models.</li> <li><em>validation set:</em> Contains 25% of the whole data set and includes ground truth data. Use this set to evaluate and optimize your models.</li> <li><em>test set:</em> Contains 25% of the whole data set. For the documents on the test set, you are not given ground truth data. This set is used for evaluation.</li> </ul> <p><br> <strong>Input Format</strong></p> <p>Both dataset-narrow and dataset-wide are based on user posts from various sites of the StackExchange network, covering different topics. We refer to each input problem (i.e., the document for which to detect style changes) by an ID, which is subsequently also used to identify the submitted solution to this input problem.</p> <p>The structure of the provided datasets is as follows:</p> <p> </p> <pre><code class="language-json">train/ dataset-narrow/ dataset-wide/ validation/ dataset-narrow/ dataset-wide/ test/ dataset-narrow/ dataset-wide/</code></pre> <p> </p> <p>For each problem instance X (i.e., each input document), two files are provided:</p> <p>problem-X.txt contains the actual text, where paragraphs are denoted by \n\n.<br> truth-problem-X.json contains the ground truth, i.e., the correct solution in JSON format:</p> <pre><code class="language-json">{ "authors": NUMBER_OF_AUTHORS, "structure": ORDER_OF_AUTHORS, "site": SOURCE_SITE, "multi-author": RESULT_TASK1, "changes": RESULT_ARRAY_TASK2 }</code></pre> <p>The result for task 1 (key "multi-author") is a binary value (1 if the document is multi-authored, 0 if the document is single-authored). The result for task 2 (key "changes") is represented as an array, holding a binary for each pair of consecutive paragraphs within the document (0 if there was no style change, 1 if there was a style change). If the document is single-authored, the solution to task 2 is an array filled with 0s. Furthermore, we provide the order of authors contained in the document (e.g., [A1, A2, A1] for a two-author document), the total number of authors and the Stackoverflow site the texts were extracted from (i.e., topic).</p> <p>An example of a multi-author document, where there was a style change between the third and fourth paragraph could look as follows (we only list the two relevant key/value pairs here): </p> <pre><code class="language-json">{ "multi-author": 1, "changes": [0,0,1,...] }</code></pre> <p>A single-author document would have the following form (again, only listing the two relevant key/value pairs):</p> <pre><code class="language-json">{ "multi-author": 0, "changes": [0,0,0,...] }</code></pre> <p> </p>
PAN22 Authorship Analysis: Authorship Verification
<p><strong>Download</strong></p> <p>Access to our corpus can be requested via the Aston Institute for Forensic Linguistics Databank: <a href="https://fold.aston.ac.uk/handle/123456789/17">https://fold.aston.ac.uk/handle/123456789/17</a></p> <p><strong>Task</strong></p> <p>Authorship verification is the task of deciding whether two texts have been written by the same author based on comparing the texts' writing styles. In previous editions of PAN, we explored the effectiveness of authorship verification technology in several languages and text genres. In the two most recent editions, cross-domain authorship verification using fanfiction texts was examined. Despite certain differences between fandoms, the task of cross-fandom authorship verification has proved to be relatively feasible. In the current edition, we focus on more challenging scenarios where each author verification case considers two texts that belong to different DTs (cross-DT authorship verification). This will allow us to study the ability of stylometric approaches to capture authorial characteristics that remain stable across DTs even when very different forms of expression are imposed by the DT norms.</p> <p>Based on a new corpus in English, we provide cross-DT authorship verification cases using the following DTs:</p> <ul> <li>Essays</li> <li>Emails</li> <li>Text messages</li> <li>Business memos</li> </ul> <p>The corpus comprises texts of around 100 individuals. All individuals have similar age (18-22) and are native English speakers. The topic of text samples is not restricted while the level of formality can vary within a certain DT (e.g., text messages may be addressed to family members or non-familial acquaintances).</p> <p>More information at: <a href="https://pan.webis.de/clef22/pan22-web/author-identification.html">Authorship Verification 2022</a></p>
VeriDark SilkRoad1 Authorship Verification Dataset
<pre>VeriDark (Authorship Verification in the DarkNet) is a benchmark for evaluating authorship analysis methods in a cybersecurity context, by introducing datasets gathered from the DarkNet marketplace forums or from Darknet-related discussions on Reddit. This benchmark contains three datasets for authorship verification and one dataset for authorship identification.</pre>
VeriDark DarkReddit+ Authorship Verification Dataset
<p>VeriDark (Authorship Verification in the DarkNet) is a benchmark for evaluating authorship analysis methods in a cybersecurity context, by introducing datasets gathered from the DarkNet marketplace forums or from Darknet-related discussions on Reddit. This benchmark contains three datasets for authorship verification and one dataset for authorship identification.</p>
VeriDark DarkReddit+ Authorship Identification Dataset
<pre>VeriDark (Authorship Verification in the DarkNet) is a benchmark for evaluating authorship analysis methods in a cybersecurity context, by introducing datasets gathered from the DarkNet marketplace forums or from Darknet-related discussions on Reddit. This benchmark contains three datasets for authorship verification and one dataset for authorship identification. </pre>
VeriDark Agora Authorship Verification Dataset
<pre>VeriDark (Authorship Verification in the DarkNet) is a benchmark for evaluating authorship analysis methods in a cybersecurity context, by introducing datasets gathered from the DarkNet marketplace forums or from Darknet-related discussions on Reddit. This benchmark contains three datasets for authorship verification and one dataset for authorship identification.</pre>
PAN21 Authorship Analysis: Style Change Detection
<p>This is the dataset for the <a href="https://pan.webis.de/clef21/pan21-web/style-change-detection.html">Style Change Detection</a> task of PAN 2021.</p> <p>The goal of the style change detection task is to identify text positions within a given multi-author document at which the author switches. </p> <p><strong>Tasks</strong></p> <p>Given a document, we ask participants to answer the following three questions:</p> <ul> <li><em>Single vs. Multiple.</em> Given a text, find out whether the text is written by a single author or by multiple authors (task 1).</li> <li><em>Style Change Basic.</em> Given a text written by two or more authors and that contains a number of style changes, find the position of the changes (task 2).</li> <li><em>Style Change Real-World.</em> Given a text written by two or more authors, find all positions of writing style change, i.e., assign all paragraphs of the text uniquely to some author out of the number of authors you assume for the multi-author document (task 3).</li> </ul> <p>All documents are provided in English and may contain an arbitrary number of style changes, resulting from at most five different authors. However, style changes may only occur between paragraphs (i.e., a single paragraph is always authored by a single author and does not contain any style changes).</p> <p><strong>Data</strong></p> <p>The dataset is split into three parts:</p> <ol> <li><em>training set:</em> Contains 70% of the whole data set and includes ground truth data. Use this set to develop and train your models.</li> <li><em>validation set:</em> Contains 15% of the whole data set and includes ground truth data. Use this set to evaluate and optimize your models.</li> <li><em>test set:</em> Contains 15% of the whole data set. For the documents on the test set, you are not given ground truth data. This set is used for evaluation.</li> </ol> <p>The dataset is based on user posts from various sites of the StackExchange network, covering different topics. We refer to each input problem (i.e., the document for which to detect style changes) by an ID, which is subsequently also used to identify the submitted solution to this input problem. We provide one folder for train, validation, and test data.</p> <p>For each problem instance <code>X</code> (i.e., each input document), two files are provided:</p> <ol> <li><code>problem-X.txt</code> contains the actual text, where paragraphs are denoted by <code>\n\n</code>.</li> <li><code>truth-problem-X.json</code> contains the ground truth, i.e., the correct solution in JSON format: <pre><code class="language-json">{ "authors": NUMBER_OF_AUTHORS, "site": SOURCE_SITE, "multi-author": RESULT_TASK1, "changes": RESULT_ARRAY_TASK2, "paragraph-authors": RESULT_ARRAY_TASK3 }</code></pre> The result for task 1 (key "multi-author") is a binary value (1 if the document is multi-authored, 0 if the document is single-authored). The result for task 2 (key "changes") is represented as an array, holding a binary for each pair of consecutive paragraphs within the document (0 if there was no style change, 1 if there was a style change). If the document is single-authored, the solution to task 2 is an array filled with 0s. For task 3 (key "paragraph-authors"), the result is the order of authors contained in the document (e.g., <code>[1, 2, 1]</code> for a two-author document), where the first author is "1", the second author appearing in the document is referred to as "2", etc. Furthermore, we provide the total number of authors and the Stackoverflow site the texts were extracted from (i.e., topic).<br> <br> An example of a multi-author document, where there was a style change between the third and fourth paragraph could look as follows (we only list the relevant key/value pairs here): <pre><code class="language-json">{ "multi-author": 1, "changes": [0,0,1,...], "paragraph-authors": [1,1,1,2,...] }</code></pre> A single-author document would have the following form (again, only listing the relevant key/value pairs): <pre><code class="language-json">{ "multi-author": 0, "changes": [0,0,0,...], "paragraph-authors": [1,1,1,...] }</code></pre> <p> </p> </li> </ol>
PAN20 Authorship Analysis: Style Change Detection
<p>This is the data set for the <a href="https://pan.webis.de/clef20/pan20-web/style-change-detection.html">Style Change Detection task</a> of PAN 2020.</p> <p>The goal of the style change detection task is to identify text positions within a given multi-author document at which the author switches. Detecting these positions is a crucial part of the authorship identification process, and for multi-author document analysis in general. Note that, for this task, we make the assumption that a change in writing style always signifies a change in author.</p> <p><strong>Tasks</strong></p> <p>Given a document, we ask participants to answer the following two questions:</p> <ul> <li>Was the given document written by multiple authors? (task 1)</li> <li>For each pair of consecutive paragraphs in the given document: is there a style change between these paragraphs? (task 2)</li> </ul> <p>In other words, the goal is to determine whether the given document contains style changes and if it indeed does, we aim to find the position of the change in the document (between paragraphs).</p> <p>All documents are provided in English and may contain zero up to ten style changes, resulting from at most three different authors. However, style changes may only occur between paragraphs (i.e., a single paragraph is always authored by a single author and does not contain any style changes).</p> <p><strong>Data</strong></p> <p>To develop and then test your algorithms, two data sets including ground truth information are provided. Those data sets differ in their topical breadth (i.e., the number of different topics that are covered in the documents contained). <em>dataset-narrow</em> contains texts from a relatively narrow set of subjects matters (all related to technology), whereas <em>dataset-wide</em> adds additional subject areas to that (travel, philosophy, economics, history, etc.).</p> <p>Both of those data sets are split into three parts:</p> <ol> <li><em>training set:</em> Contains 50% of the whole data set and includes ground truth data. Use this set to develop and train your models.</li> <li><em>validation set:</em> Contains 25% of the whole data set and includes ground truth data. Use this set to evaluate and optimize your models.</li> <li><em>test set:</em> Contains 25% of the whole data set. For the documents on the test set, you are not given ground truth data. This set is used for evaluation (see later).</li> </ol> <p> </p> <p><strong>Input Format</strong></p> <p>Both dataset-narrow and dataset-wide are based on user posts from various sites of the StackExchange network, covering different topics. We refer to each input problem (i.e., the document for which to detect style changes) by an ID, which is subsequently also used to identify the submitted solution to this input problem.</p> <p>The structure of the provided datasets is as follows:</p> <pre> <code> train/ dataset-narrow/ dataset-wide/ validation/ dataset-narrow/ dataset-wide/ test/ dataset-narrow/ dataset-wide/ </code></pre> <p>For each problem instance <code>X</code> (i.e., each input document), two files are provided:</p> <ol> <li><code>problem-X.txt</code> contains the actual text, where paragraphs are denoted by <code>\n\n</code>.</li> <li><code>truth-problem-X.json</code> contains the ground truth, i.e., the correct solution in JSON format: <pre><code>{ "authors": NUMBER_OF_AUTHORS, "structure": ORDER_OF_AUTHORS, "site": SOURCE_SITE, "multi-author": RESULT_TASK1, "changes": RESULT_ARRAY_TASK2 }</code></pre> <p>The result for task 1 (key "multi-author") is a binary value (1 if the document is multi-authored, 0 if the document is single-authored). The result for task 2 (key "changes") is represented as an array, holding a binary for each pair of consecutive paragraphs within the document (0 if there was no style change, 1 if there was a style change). If the document is single-authored, the solution to task 2 is an array filled with 0s. Furthermore, we provide the order of authors contained in the document (e.g., <code>[A1, A2, A1]</code> for a two-author document), the total number of authors and the Stackoverflow site the texts were extracted from (i.e., topic).</p> <p>An example of a multi-author document, where there was a style change between the third and fourth paragraph could look as follows (we only list the two relevant key/value pairs here): </p> <pre><code>{ "multi-author": 1, "changes": [0,0,1,...] }</code></pre> <p>A single-author document would have the following form (again, only listing the two relevant key/value pairs):</p> <pre><code>{ "multi-author": 0, "changes": [0,0,0,...] }</code></pre> </li> </ol> <p> </p>
SMAuC - The Scientific Multi-Authorship Corpus
<p>The rapidly growing volume of scientific publications offers an interesting challenge for research on methods for analyzing the authorship of documents with one or more authors. However, most existing datasets are inadequate, lacking scientific documents or necessary metadata. We introduce SMAuC, a comprehensive, metadata-rich corpus tailored for scientific authorship analysis. Comprising over 3 million publications across various disciplines from over 5 million authors, SMAuC is the largest openly accessible corpus for this purpose. It encompasses scientific texts from humanities and natural sciences, accompanied by extensive, curated metadata, including unambiguous author IDs. SMAuC aims to significantly advance the domain of authorship analysis in scientific texts.</p>
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