Proceedings of the NAACL Workshop on Creating Speech and Text Language Data With Amazon's Mechanical Turk

Annotating named entities in Twitter data with crowdsourcing

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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.”

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amazon mechanical turk, crowdsourcing, information extraction, mturk, named entities, named entity recognition, natural language processing, social media, social, twitter, word clouds


Association for Computational Linguistics

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