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12 results for “author identification”
Pinterest dataset for age and gender identification in author profiling
<p>This dataset was used for the experiments presented in the article "Reconstructive Classification for Age and Gender Identification in Social Networks" - IEEE Transactions on Computational Social Systems</p> <p>The dataset contains text data from 548,761 pins corresponding to 264 users of Pinterest.</p> <p>There are 7 files.</p> <p>The first 5 files correspond to the extracted textual features from the pins that are aggregated per user: ats, emojis/emoticons, hashtags, links, and words.</p> <p>There are 264 lines in each file (one per user), as the concatenation of the extracted features from all the pins corresponding to each user.</p> <p>The last 2 files are the labels for the age and gender of the users. There are also 264 lines (one per user).</p> <p>For age, there are 4 possible labels: 18-24, 25-34, 35-46, and 50+</p> <p>For gender, there are 2 possible labels: F and M</p>
РИС. 5. ИЗвестные находки Monacha cartusiana на Западе Украины. A. Анатомически проверенные авторами статьи. B. Определенные только по раковинам или беЗ учета анатомических раЗличий между M. cartusiana и M. claustralis. ИЗЗа масШтаба картосхем находки в блиЗко расположенных населенных пунктах объединены в одну точку. FIG. 5. Known records of Monacha cartusiana in Western Ukraine. A. Anatomically examined by the authors of this paper. B. Identifed only by shell or without regard to anatomical differences between M. cartusiana and M. claustralis. Due to the scale of the schematic maps, the findings in closely located settlements are combined into one point. in Monacha claustralis и M. cartusiana (Gastropoda, Hygromiidae) - два криптических вида антропохорных наЗемных моллюсков на Западе Украины
РИС. 5. ИЗвестные находки Monacha cartusiana на Западе Украины. A. Анатомически проверенные авторами статьи. B. Определенные только по раковинам или беЗ учета анатомических раЗличий между M. cartusiana и M. claustralis. ИЗЗа масШтаба картосхем находки в блиЗко расположенных населенных пунктах объединены в одну точку. FIG. 5. Known records of Monacha cartusiana in Western Ukraine. A. Anatomically examined by the authors of this paper. B. Identifed only by shell or without regard to anatomical differences between M. cartusiana and M. claustralis. Due to the scale of the schematic maps, the findings in closely located settlements are combined into one point.
PAN12 Author Identification: Attribution
<p>We provide you with a training corpus that comprises several different common attribution and clustering scenarios.</p> <p>In last year’s competition, the corpus consisted of several thousand relatively small documents, with distractor sets consisting of hundreds of authors. This was considered to create impracticalities for many participants, especially those that relied upon machine-aided instead of fully automatic analysis. We have instead focused on a smaller group of larger documents, perhaps more typical of the type of cases usually analyzed by “traditional” close reading.</p> <p>Last year’s corpus was taken from the Enron email corpus; this years instead was collected from the free fiction collection published by Feedbooks.com, including both classic fiction that is now out-of-copyright as well as (fiction, represented by the Feedbooks.com site). This of course introduces the standard issue of analysis-by-Google, but that’s a very difficult problem to avoid short of generating content to order.</p>
PAN13 Author Identification: Verification
<p>We provide you with a training data set that consists of documents written in both English and Spanish. With regard to age, we will consider posts of three classes: 10s (13-17), 20s (23-27), and 30s (33-47). Moreover, documents from authors who pretend to be minors will be included (e.g., documents composed of chat lines of sexual predators will be also considered). <a href="https://www.uni-weimar.de/medien/webis/events/pan-13/pan13-papers-final/pan13-author-profiling/rangel13-overview.pdf#page=3">Learn more »</a></p>
PAN17 Author Identification: Clustering
<p>We provide a collection of (up to 50) short documents (paragraphs extracted from larger documents), identify authorship links and groups of documents by the same author. All documents are single-authored, in the same language, and belong to the same genre. However, the topic or text-length of documents may vary. The number of distinct authors whose documents are included in the collection is not given.</p> <p>More information: <a href="https://pan.webis.de/clef17/pan17-web/author-clustering.html">Link</a></p>
Houvardas06 Author Identification: C50-Attribution
<p>This dataset contains 2500 texts from 50 different authors/candidates (C50). The ground truth can be found inside a json-file.</p>
PAN11 Author Identification: Attribution
<p>We provide you with a training corpus that comprises several different common attribution and verification scenarios. There are five training collections consisting of real-world texts (for authorship attribution), and three each with a single author (for authorship verification).</p>
PAN14 Author Identification: Verification
<p>We provide you with a training corpus that comprises a set of author verification problems in several languages/genres. Each problem consists of some (up to five) known documents by a single person and exactly one questioned document. All documents within a single problem instance will be in the same language and best efforts are applied to assure that within-problem documents are matched for genre, register, theme, and date of writing. The document lengths vary from a few hundred to a few thousand words.</p> <p>More information: <a href="https://pan.webis.de/clef14/pan14-web/authorship-verification.html">Link</a></p>
PAN18 Author Identification: Attribution
<p>We provide a corpus which comprises a set of cross-domain authorship attribution problems in each of the following 5 languages: English, French, Italian, Polish, and Spanish. Note that we specifically avoid to use the term 'training corpus' because <strong>the sets of candidate authors of the development and the evaluation corpora are not overlapping</strong>. Therefore, your approach should not be designed to particularly handle the candidate authors of the development corpus.</p> <p>Each problem consists of a set of known fanfics by each candidate author and a set of unknown fanfics located in separate folders. The file <code>problem-info.json</code> that can be found in the main folder of each problem, shows the name of folder of unknown documents and the list of names of candidate author folders.</p> <p>The true author of each unknown document can be seen in the file <code>ground-truth.json</code>, also found in the main folder of each problem.</p> <p>In addition, to handle a collection of such problems, the file <code>collection-info.json</code>includes all relevant information. In more detail, for each problem it lists its main folder, the language (either <code>"en"</code>, <code>"fr"</code>, <code>"it"</code>, <code>"pl"</code>, or <code>"sp"</code>) and encoding (always <code>UTF-8</code>) of its documents.</p> <p>More information: <a href="https://pan.webis.de/clef18/pan18-web/authorship-attribution.html">Link</a></p>
PAN16 Author Identification: Clustering
<p>We provide a collection of (up to 100) documents to identify authorship links and groups of documents by the same author. All documents are single-authored, in the same language, and belong to the same genre. However, the topic or text-length of documents may vary. The number of distinct authors whose documents are included in the collection is not given.</p> <p>More information: <a href="https://pan.webis.de/clef16/pan16-web/author-clustering.html">Link</a></p>
PAN15 Author Identification: Verification
<p>We provide you with a training corpus that comprises a set of author verification problems in several languages/genres. Each problem consists of some (up to five) known documents by a single person and exactly one questioned document. All documents within a single problem instance will be in the same language. However, their genre and/or topic may differ significantly. The document lengths vary from a few hundred to a few thousand words.</p> <p>The documents of each problem are located in a separate folder, the name of which (problem ID) encodes the language of the documents. The following list shows the available sub-corpora, including their language, type (cross-genre or cross-topic), code, and examples of problem IDs:</p> <p>Language; Type; Code; Problem IDs<br> Dutch; Cross-genre; DU; DU001, DU002, DU003, etc.<br> English; Cross-topic; EN; EN001, EN002, EN003, etc.<br> Greek; Cross-topic; GR; GR001, GR002, GR003, etc.<br> Spanish; Cross-genre; SP; SP001, SP002, SP003, etc.</p> <p>The ground truth data of the training corpus found in the file <code>truth.txt</code> include one line per problem with problem ID and the correct binary answer (Y means the known and the questioned documents are by the same author and N means the opposite). For example:</p> <pre>EN001 N EN002 Y EN003 N ...</pre>
Stamatatos06 Author Identification: C10-Attribution
<p>This dataset contains 500 texts from 10 different authors/candidates (C10). The ground truth can be found inside a json-file.</p>
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