Annotating named entities in Twitter data with crowdsourcing

  • Authors:

  • Tim Finin;Will Murnane;Anand Karandikar;Nicholas Keller;Justin Martineau;Mark Dredze

  • Affiliations:

  • University of Maryland, Baltimore, MD;University of Maryland, Baltimore, MD;University of Maryland, Baltimore, MD;University of Maryland, Baltimore, MD;University of Maryland, Baltimore, MD;Johns Hopkins University, Baltimore, MD

  • Venue:
  • CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
  • Year:
  • 2010

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Abstract

We 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."