Self-Crowdsourcing Training for Relation Extraction

<p dir="ltr">One expensive step when defining crowdsourcing tasks is to define the examples and control questions for instructing the crowd workers. In this paper, we introduce a self-training strategy for crowdsourcing. The main idea is to use an automatic classifier, trained on wea...

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Main Author: Azad Abad (19691692) (author)
Other Authors: Moin Nabi (19691695) (author), Alessandro Moschitti (19691683) (author)
Published: 2017
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author Azad Abad (19691692)
author2 Moin Nabi (19691695)
Alessandro Moschitti (19691683)
author2_role author
author
author_facet Azad Abad (19691692)
Moin Nabi (19691695)
Alessandro Moschitti (19691683)
author_role author
dc.creator.none.fl_str_mv Azad Abad (19691692)
Moin Nabi (19691695)
Alessandro Moschitti (19691683)
dc.date.none.fl_str_mv 2017-07-01T03:00:00Z
dc.identifier.none.fl_str_mv 10.18653/v1/p17-2082
dc.relation.none.fl_str_mv https://figshare.com/articles/conference_contribution/Self-Crowdsourcing_Training_for_Relation_Extraction/27050734
dc.rights.none.fl_str_mv CC BY 4.0
info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Information and computing sciences
Artificial intelligence
Human-centred computing
Language, communication and culture
Linguistics
Crowdsourcing
Self-training strategy
Automatic classifier
Weakly supervised data
High confidence examples
dc.title.none.fl_str_mv Self-Crowdsourcing Training for Relation Extraction
dc.type.none.fl_str_mv Text
Conference contribution
info:eu-repo/semantics/publishedVersion
text
conference object
description <p dir="ltr">One expensive step when defining crowdsourcing tasks is to define the examples and control questions for instructing the crowd workers. In this paper, we introduce a self-training strategy for crowdsourcing. The main idea is to use an automatic classifier, trained on weakly supervised data, to select examples associated with high confidence. These are used by our automatic agent to explain the task to crowd workers with a question answering approach. We compared our relation extraction system trained with data annotated (i) with distant supervision and (ii) by workers instructed with our approach. The analysis shows that our method relatively improves the relation extraction system by about 11% in F1.</p><h2>Other Information</h2><p dir="ltr">Published in: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See conference contribution on publisher's website: <a href="https://dx.doi.org/10.18653/v1/p17-2082" target="_blank">https://dx.doi.org/10.18653/v1/p17-2082</a></p><p dir="ltr">Conference information: 55th Annual Meeting of the Association for Computational Linguistics (Short Papers), pages 518–523 Vancouver, Canada, July 30 - August 4, 2017</p>
eu_rights_str_mv openAccess
id Manara2_337e78487321026373ca41b897e85f8f
identifier_str_mv 10.18653/v1/p17-2082
network_acronym_str Manara2
network_name_str Manara2
oai_identifier_str oai:figshare.com:article/27050734
publishDate 2017
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rights_invalid_str_mv CC BY 4.0
spelling Self-Crowdsourcing Training for Relation ExtractionAzad Abad (19691692)Moin Nabi (19691695)Alessandro Moschitti (19691683)Information and computing sciencesArtificial intelligenceHuman-centred computingLanguage, communication and cultureLinguisticsCrowdsourcingSelf-training strategyAutomatic classifierWeakly supervised dataHigh confidence examples<p dir="ltr">One expensive step when defining crowdsourcing tasks is to define the examples and control questions for instructing the crowd workers. In this paper, we introduce a self-training strategy for crowdsourcing. The main idea is to use an automatic classifier, trained on weakly supervised data, to select examples associated with high confidence. These are used by our automatic agent to explain the task to crowd workers with a question answering approach. We compared our relation extraction system trained with data annotated (i) with distant supervision and (ii) by workers instructed with our approach. The analysis shows that our method relatively improves the relation extraction system by about 11% in F1.</p><h2>Other Information</h2><p dir="ltr">Published in: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See conference contribution on publisher's website: <a href="https://dx.doi.org/10.18653/v1/p17-2082" target="_blank">https://dx.doi.org/10.18653/v1/p17-2082</a></p><p dir="ltr">Conference information: 55th Annual Meeting of the Association for Computational Linguistics (Short Papers), pages 518–523 Vancouver, Canada, July 30 - August 4, 2017</p>2017-07-01T03:00:00ZTextConference contributioninfo:eu-repo/semantics/publishedVersiontextconference object10.18653/v1/p17-2082https://figshare.com/articles/conference_contribution/Self-Crowdsourcing_Training_for_Relation_Extraction/27050734CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/270507342017-07-01T03:00:00Z
spellingShingle Self-Crowdsourcing Training for Relation Extraction
Azad Abad (19691692)
Information and computing sciences
Artificial intelligence
Human-centred computing
Language, communication and culture
Linguistics
Crowdsourcing
Self-training strategy
Automatic classifier
Weakly supervised data
High confidence examples
status_str publishedVersion
title Self-Crowdsourcing Training for Relation Extraction
title_full Self-Crowdsourcing Training for Relation Extraction
title_fullStr Self-Crowdsourcing Training for Relation Extraction
title_full_unstemmed Self-Crowdsourcing Training for Relation Extraction
title_short Self-Crowdsourcing Training for Relation Extraction
title_sort Self-Crowdsourcing Training for Relation Extraction
topic Information and computing sciences
Artificial intelligence
Human-centred computing
Language, communication and culture
Linguistics
Crowdsourcing
Self-training strategy
Automatic classifier
Weakly supervised data
High confidence examples