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An empirical evaluation of electronic annotation tools for Twitter data
Despite a growing number of natural language processing shared-tasks dedicated to the use of Twitter data, there is currently no ad-hoc annotation tool for the purpose. During the 6th edition of Biomedical Linked Annotation Hackathon (BLAH), after a short review of 19 generic annotation tools, we ad...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korea Genome Organization
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7362942/ https://www.ncbi.nlm.nih.gov/pubmed/32634878 http://dx.doi.org/10.5808/GI.2020.18.2.e24 |
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author | Weissenbacher, Davy O'Connor, Karen Hiraki, Aiko T. Kim, Jin-Dong Gonzalez-Hernandez, Graciela |
author_facet | Weissenbacher, Davy O'Connor, Karen Hiraki, Aiko T. Kim, Jin-Dong Gonzalez-Hernandez, Graciela |
author_sort | Weissenbacher, Davy |
collection | PubMed |
description | Despite a growing number of natural language processing shared-tasks dedicated to the use of Twitter data, there is currently no ad-hoc annotation tool for the purpose. During the 6th edition of Biomedical Linked Annotation Hackathon (BLAH), after a short review of 19 generic annotation tools, we adapted GATE and TextAE for annotating Twitter timelines. Although none of the tools reviewed allow the annotation of all information inherent of Twitter timelines, a few may be suitable provided the willingness by annotators to compromise on some functionality. |
format | Online Article Text |
id | pubmed-7362942 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Korea Genome Organization |
record_format | MEDLINE/PubMed |
spelling | pubmed-73629422020-07-23 An empirical evaluation of electronic annotation tools for Twitter data Weissenbacher, Davy O'Connor, Karen Hiraki, Aiko T. Kim, Jin-Dong Gonzalez-Hernandez, Graciela Genomics Inform Opinion Despite a growing number of natural language processing shared-tasks dedicated to the use of Twitter data, there is currently no ad-hoc annotation tool for the purpose. During the 6th edition of Biomedical Linked Annotation Hackathon (BLAH), after a short review of 19 generic annotation tools, we adapted GATE and TextAE for annotating Twitter timelines. Although none of the tools reviewed allow the annotation of all information inherent of Twitter timelines, a few may be suitable provided the willingness by annotators to compromise on some functionality. Korea Genome Organization 2020-06-17 /pmc/articles/PMC7362942/ /pubmed/32634878 http://dx.doi.org/10.5808/GI.2020.18.2.e24 Text en (c) 2020, Korea Genome Organization (CC) This is an open-access article distributed under the terms of the Creative Commons Attribution license(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Opinion Weissenbacher, Davy O'Connor, Karen Hiraki, Aiko T. Kim, Jin-Dong Gonzalez-Hernandez, Graciela An empirical evaluation of electronic annotation tools for Twitter data |
title | An empirical evaluation of electronic annotation tools for Twitter data |
title_full | An empirical evaluation of electronic annotation tools for Twitter data |
title_fullStr | An empirical evaluation of electronic annotation tools for Twitter data |
title_full_unstemmed | An empirical evaluation of electronic annotation tools for Twitter data |
title_short | An empirical evaluation of electronic annotation tools for Twitter data |
title_sort | empirical evaluation of electronic annotation tools for twitter data |
topic | Opinion |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7362942/ https://www.ncbi.nlm.nih.gov/pubmed/32634878 http://dx.doi.org/10.5808/GI.2020.18.2.e24 |
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