DOI: 10.5281/zenodo.3530362
title: PAN19 Authorship Analysis: Style Change Detection

description: Many approaches have been proposed recently to identify <em>the</em> author of a given document. Thereby, one fact is often silently assumed: i.e., that the given document is indeed written by only author. For a realistic author identification system it is therefore crucial to at first determine whether a document is single- or multiauthored. To this end, previous PAN editions aimed to analyze multi-authored documents. As it has been shown that it is a hard problem to reliably identify individual authors and their contribution within a single document (Author Diarization, 2016; Style Breach Detection, 2017), last year's task substantially relaxed the problem by asking only for binary decision (single- or multi-authored). Considering the promising results achieved by the submitted approaches (see the overview paper for details), we continue last year's task and additionally ask participants to predict the number of involved authors. Given a document, participants thus should apply intrinsic style analyses to hierarchically answer the following questions: Is the document written by one or more authors, i.e., do style changes exist or not? If it is multi-authored, how many authors have collaborated? All documents are provided in English and may contain zero up to arbitrarily many style changes, resulting from arbitrarily many authors. The <strong>training</strong> set: contains 50% of the whole dataset and includes solutions. Use this set to feed/train your models. Like last year, the whole data set is based on user posts from various sites of the StackExchange network, covering different topics and containing approximately 300 to 2000 tokens per document. For each problem instance X, two files are provided: <code>problem-X.txt</code> contains the actual text <code>problem-X.truth</code> contains the ground truth, i.e., the correct solution in JSON format: <pre><code class="language-json">{ "authors": number_of_authors, "structure": [author_segment_1, ..., author_segment_3], "switches": [ character_pos_switch_segment_1, ..., character_pos_switch_segment_n, ] }</code></pre> An example for a multi-author document could look as follows: <pre><code class="language-json">{ "authors": 4, "structure": ["A1", "A2", "A4", "A2", "A4", "A2", "A3", "A2", "A4"], "switches": [805, 1552, 2827, 3584, 4340, 5489, 7564, 8714] }</code></pre> whereas a single-author document would have exactly the following form: <pre><code class="language-json">{ "authors": 1, "structure": ["A1"], "switches": [] }</code></pre> Note that authors within the <em>structure</em> correspond only to the respective document, i.e., they are not the same over the whole dataset. For example, author <em>A1</em> in document 1 is most likely <strong>not</strong> the same author as <em>A1</em> in document 2 (it <strong>could</strong> be, but as there are hundreds of authors the chances are very small that this is the case). Further, please consider that the structure and the <em>switches</em> are provided only as additional resources for the development of your algorithms, i.e., they are <strong>not expected to be predicted</strong>. To tackle the problem, you can develop novel approaches, extend existing algorithms from last year's task or adapt approaches from related problems such as <strong>intrinsic plagiarism detection</strong> or <strong>text segmentation</strong>. You are also free to additionally evaluate your approaches on last year's training/validation/test dataset (for the number of authors use the corresponding meta data).
description: {"references": ["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."]}