License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.LDK.2021.12
URN: urn:nbn:de:0030-drops-145481
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2021/14548/
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Pecòre, Stefania

Supporting the Annotation Experience Through CorEx and Word Mover’s Distance

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OASIcs-LDK-2021-12.pdf (1 MB)

Abstract

Online communities can be used to promote destructive behaviours, as in pro-Eating Disorder (ED) communities. Research needs annotated data to study these phenomena. Even though many platforms have already moderated this type of content, Twitter has not, and it can still be used for research purposes. In this paper, we unveiled emojis, words, and uncommon linguistic patterns within the ED Twitter community by using the Correlation Explanation (CorEx) algorithm on unstructured and non-annotated data to retrieve the topics. Then we annotated the dataset following these topics. We analysed then the use of CorEx and Word Mover’s Distance to retrieve automatically similar new sentences and augment the annotated dataset.

BibTeX - Entry

@InProceedings{pecore:OASIcs.LDK.2021.12,
  author =	{Pec\`{o}re, Stefania},
  title =	{{Supporting the Annotation Experience Through CorEx and Word Mover’s Distance}},
  booktitle =	{3rd Conference on Language, Data and Knowledge (LDK 2021)},
  pages =	{12:1--12:15},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-199-3},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{93},
  editor =	{Gromann, Dagmar and S\'{e}rasset, Gilles and Declerck, Thierry and McCrae, John P. and Gracia, Jorge and Bosque-Gil, Julia and Bobillo, Fernando and Heinisch, Barbara},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2021/14548},
  URN =		{urn:nbn:de:0030-drops-145481},
  doi =		{10.4230/OASIcs.LDK.2021.12},
  annote =	{Keywords: topic retrieval, annotation, eating disorders, natural language processing}
}

Keywords: topic retrieval, annotation, eating disorders, natural language processing
Collection: 3rd Conference on Language, Data and Knowledge (LDK 2021)
Issue Date: 2021
Date of publication: 30.08.2021

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