Annotating named entities in Twitter data with crowdsourcing

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dc.contributor.authorFinin, Tim
dc.contributor.authorMurnane, Will
dc.contributor.authorKarandikar, Anand
dc.contributor.authorKeller, Nicholas
dc.contributor.authorMartineau, Justin
dc.contributor.authorDredze, Mark
dc.date.accessioned2018-11-13T19:53:55Z
dc.date.available2018-11-13T19:53:55Z
dc.date.issued2010-06-06
dc.descriptionProceedings of the NAACL Workshop on Creating Speech and Text Language Data With Amazon's Mechanical Turken_US
dc.description.abstractWe describe our experience using both Amazon Mechanical Turk (MTurk) and Crowd Flower to collect simple named entity annotations for Twitter status updates. Unlike most genres that have traditionally been the focus of named entity experiments, Twitter is far more informal and abbreviated. The collected annotations and annotation techniques will provide a first step towards the full study of named entity recognition in domains like Facebook and Twitter. We also briefly describe how to use MTurk to collect judgements on the quality of “word clouds.”en_US
dc.description.sponsorshipThis work was done with partial support from the Office of Naval Research and the Johns Hopkins University Human Language Technology Center of Excellence. We thank both Amazon and Dolores Labs for grants that allowed us to use their systems for the experiments.en_US
dc.description.urihttps://ebiquity.umbc.edu/paper/html/id/476/Annotating-named-entities-in-Twitter-data-with-crowdsourcingen_US
dc.format.extent9 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/M21J97C2P
dc.identifier.citationTim Finin, Will Murnane, Anand Karandikar, Nicholas Keller, Justin Martineau, and Mark Dredze, Annotating named entities in Twitter data with crowdsourcing, Proceedings of the NAACL Workshop on Creating Speech and Text Language Data With Amazon's Mechanical Turk, 2010. https://aclanthology.org/W10-0713/en_US
dc.identifier.urihttp://hdl.handle.net/11603/11974
dc.language.isoen_USen_US
dc.publisherAssociation for Computational Linguisticsen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectamazon mechanical turken_US
dc.subjectcrowdsourcingen_US
dc.subjectinformation extractionen_US
dc.subjectmturken_US
dc.subjectnamed entitiesen_US
dc.subjectnamed entity recognitionen_US
dc.subjectnatural language processingen_US
dc.subjectsocial mediaen_US
dc.subjectsocialen_US
dc.subjecttwitteren_US
dc.subjectword cloudsen_US
dc.subjectUMBC Ebiquity Research Groupen_US
dc.titleAnnotating named entities in Twitter data with crowdsourcingen_US
dc.typeTexten_US

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