Annotating named entities in Twitter data with crowdsourcing

Authors: , Will Murnane, Anand Karandikar, Nicholas Keller, Justin Martineau, Mark Dredze

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


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.”

Type: InProceedings

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

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