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2 datasets found
  1. Webis Gmane Email Corpus 2019

    • zenodo.org
    Updated Jun 3, 2020
  2. Webis-Gmane-19

    • webis.de
    3766984
    Updated 2020
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Janek Bevendorff; Khalid Al-Khatib; Martin Potthast; Benno Stein (2020). Webis Gmane Email Corpus 2019 [Dataset]. http://doi.org/10.5281/zenodo.3766985
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Webis Gmane Email Corpus 2019

4 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jun 3, 2020
Dataset provided by
Bauhaus-Universität Weimarhttp://www.uni-weimar.de/
Leipzig Universityhttp://www.uni-leipzig.de/
Authors
Janek Bevendorff; Khalid Al-Khatib; Martin Potthast; Benno Stein
Description

The Webis Gmane Email Corpus 2019 is a dataset of more than 153 million parsed and segmented emails crawled between February and May 2019 from gmane.io covering more than 20 years of public mailing lists. The dataset has been published as a resource at ACL 2020.

The dataset comes as a set of Gzip-compressed files containing line-based JSON in the Elasticsearch bulk format. Each data record consists of two lines:

{"index": {"_id": "

The first line is the Elasticsearch index action with a document UUID, the second one the actual parsed email with a (reduced and anonymized) set of headers, the detected language, the original Gmane group name and the predicted content segments as character spans. The Gzip files are splittable every 1,000 records (line pairs) for parallel processing in, e.g., Hadoop.

Available email headers are:

  • message_id
  • date (yyyy-MM-dd HH:mm:ssZZ)
  • subject
  • from
  • to
  • cc
  • in_reply_to
  • references
  • list_id

Available segment classes are:

  • paragraph
  • closing
  • inline_headers
  • log_data
  • mua_signature
  • patch
  • personal_signature
  • quotation
  • quotation_marker
  • raw_code
  • salutation
  • section_heading
  • tabular
  • technical
  • visual_separator

Find more information about the dataset and the segmentation model at https://webis.de/data#webis-gmane-19">webis.de.

If you are using this resource in your work, please cite it as:

@InProceedings{stein:2020o,
 author =       {Janek Bevendorff and Khalid Al-Khatib and Martin Potthast and Benno Stein},
 booktitle =      {58th Annual Meeting of the Association for Computational Linguistics (ACL 2020)},
 month =        jul,
 publisher =      {Association for Computational Linguistics},
 site =        {Seattle, USA},
 title =        {{Crawling and Preprocessing Mailing Lists At Scale for Dialog Analysis}},
 year =        2020
}

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