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

Author Stefania Pecòre

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Stefania Pecòre
  • School of Electrical Engineering and Computer Science, University of Ottawa, Canada

Funding

IT12441 MITACS and SafeToNet Canada

Acknowledgements

We thank MITACS and SafeToNet Canada for their generous funding. In addition to this, we thank the University of Ottawa and the supervisor of the project, Professor Diana Inkpen, for their support.

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Stefania Pecòre. Supporting the Annotation Experience Through CorEx and Word Mover’s Distance. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 12:1-12:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/OASIcs.LDK.2021.12

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.

Subject Classification

ACM Subject Classification
  • Applied computing → Document management and text processing
  • Applied computing → Annotation
Keywords
  • topic retrieval
  • annotation
  • eating disorders
  • natural language processing

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