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This is the data set for the Style Change Detection task of PAN@CLEF 2019.
\n\nThe 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.
\n\nTasks
\n\nGiven a document, we ask participants to answer the following two questions:
\n\nIn 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).
\n\nAll 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).
\n\nData
\n\nTo 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.).
\n\nBoth of those data sets are split into three parts:
\n\n
\nInput Format
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.
\n\nThe structure of the provided datasets is as follows:
\n\n\n\n
train/\n\u00a0 \u00a0 dataset-narrow/\n\u00a0 \u00a0 dataset-wide/\nvalidation/\n\u00a0 \u00a0 dataset-narrow/\n\u00a0 \u00a0 dataset-wide/\ntest/\n\u00a0 \u00a0 dataset-narrow/\n\u00a0 \u00a0 dataset-wide/
\n\n\n\n
For each problem instance X (i.e., each input document), two files are provided:
\n\nproblem-X.txt contains the actual text, where paragraphs are denoted by \\n\\n.
\ntruth-problem-X.json contains the ground truth, i.e., the correct solution in JSON format:
{\n\u00a0 \u00a0 \"authors\": NUMBER_OF_AUTHORS,\n\u00a0 \u00a0 \"structure\": ORDER_OF_AUTHORS,\n\u00a0 \u00a0 \"site\": SOURCE_SITE,\n\u00a0 \u00a0 \"multi-author\": RESULT_TASK1,\n\u00a0 \u00a0 \"changes\": RESULT_ARRAY_TASK2\n}
\n\nThe 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).
\n\nAn 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):
\n\n{\n\u00a0 \u00a0 \"multi-author\": 1,\n\u00a0 \u00a0 \"changes\": [0,0,1,...]\n}
\n\nA single-author document would have the following form (again, only listing the two relevant key/value pairs):
\n\n{\n\u00a0 \u00a0 \"multi-author\": 0,\n\u00a0 \u00a0 \"changes\": [0,0,0,...]\n}
\n\n", "languages": [ { "id": "eng", "title": { "en": "English" } } ], "publication_date": "2019-01-17", "publisher": "Zenodo", "references": [ { "reference": "Eva Zangerle, Michael Tschuggnall, G\u00fcnther Specht, Martin Potthast, and Benno Stein. Overview of the Style Change Detection Task at PAN 2019. In Linda Cappellato, Nicola Ferro, David E. Losada, and Henning M\u00fcller, editors, CLEF 2019 Labs and Workshops, Notebook Papers, September 2019. CEUR-WS.org." } ], "resource_type": { "id": "dataset", "title": { "de": "Datensatz", "en": "Dataset" } }, "subjects": [ { "subject": "authorship analysis" }, { "subject": "style" }, { "subject": "detection" }, { "subject": "change" } ], "title": "PAN19 Authorship Analysis: Style Change Detection", "version": "2.0" }, "parent": { "access": { "owned_by": { "user": 79446 }, "settings": { "accept_conditions_text": "
Please request access to the data with a short statement on how you want to use it. Thanks!
\n\nWe would like to point out that you can register on pan.webis.de to be part of the PAN community.
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