Named Entity Recognition in Tweets: An Experimental Study


People tweet more than 100 Million times daily, yielding a noisy, informal, but sometimes informative corpus of 140-character messages that mirrors the zeitgeist in an unprecedented manner. The performance of standard NLP tools is severely degraded on tweets. This paper addresses this issue by re-building the NLP pipeline beginning with part-of-speech tagging, through chunking, to named-entity recognition. Our novel T-NER system doubles F1 score compared with the Stanford NER system. T-NER leverages the redundancy inherent in tweets to achieve this performance, using LabeledLDA to exploit Freebase dictionaries as a source of distant supervision. LabeledLDA outperforms cotraining, increasing F1 by 25% over ten common entity types. Our NLP tools are available at: http://

Extracted Key Phrases

12 Figures and Tables

Citations per Year

657 Citations

Semantic Scholar estimates that this publication has 657 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Ritter2011NamedER, title={Named Entity Recognition in Tweets: An Experimental Study}, author={Alan Ritter and Sam Clark and Mausam and Oren Etzioni}, booktitle={EMNLP}, year={2011} }