Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments

Kevin Gimpel; Nathan Schneider; Brendan O'Connor; Dipanjan Das; Daniel Mills; Jacob Eisenstein; Michael Heilman; Dani Yogatama; Jeffrey Flanigan; Noah A. Smith

Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments

Kevin Gimpel, Nathan Schneider, Brendan O'Connor, Dipanjan Das, Daniel Mills, Jacob Eisenstein, Michael Heilman, Dani Yogatama, Jeffrey Flanigan, Noah A. Smith

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

We address the problem of part-of-speech tagging for English data from the popular microblogging service Twitter. We develop a tagset, annotate data, develop features, and report tagging results nearing 90% accuracy. The data and tools have been made available to the research community with the goal of enabling richer text analysis of Twitter and related social media data sets.

Original language	English
Title of host publication	Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics
Publisher	Association for Computational Linguistics
Pages	42-47
Number of pages	6
Publication status	Published - 2011

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http://www.aclweb.org/anthology/P11-2008

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Gimpel, K., Schneider, N., O'Connor, B., Das, D., Mills, D., Eisenstein, J., Heilman, M., Yogatama, D., Flanigan, J., & Smith, N. A. (2011). Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics (pp. 42-47). Association for Computational Linguistics. http://www.aclweb.org/anthology/P11-2008

@inproceedings{1cf6b30c844a41edadc1d84c09fbb281,

title = "Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments",

abstract = "We address the problem of part-of-speech tagging for English data from the popular microblogging service Twitter. We develop a tagset, annotate data, develop features, and report tagging results nearing 90% accuracy. The data and tools have been made available to the research community with the goal of enabling richer text analysis of Twitter and related social media data sets.",

author = "Kevin Gimpel and Nathan Schneider and Brendan O'Connor and Dipanjan Das and Daniel Mills and Jacob Eisenstein and Michael Heilman and Dani Yogatama and Jeffrey Flanigan and Smith, {Noah A.}",

year = "2011",

language = "English",

pages = "42--47",

booktitle = "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics",

publisher = "Association for Computational Linguistics",

}

Gimpel, K, Schneider, N, O'Connor, B, Das, D, Mills, D, Eisenstein, J, Heilman, M, Yogatama, D, Flanigan, J & Smith, NA 2011, Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments. in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, pp. 42-47. <http://www.aclweb.org/anthology/P11-2008>

Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments. / Gimpel, Kevin; Schneider, Nathan; O'Connor, Brendan et al.
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2011. p. 42-47.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments

AU - Gimpel, Kevin

AU - Schneider, Nathan

AU - O'Connor, Brendan

AU - Das, Dipanjan

AU - Mills, Daniel

AU - Eisenstein, Jacob

AU - Heilman, Michael

AU - Yogatama, Dani

AU - Flanigan, Jeffrey

AU - Smith, Noah A.

PY - 2011

Y1 - 2011

N2 - We address the problem of part-of-speech tagging for English data from the popular microblogging service Twitter. We develop a tagset, annotate data, develop features, and report tagging results nearing 90% accuracy. The data and tools have been made available to the research community with the goal of enabling richer text analysis of Twitter and related social media data sets.

AB - We address the problem of part-of-speech tagging for English data from the popular microblogging service Twitter. We develop a tagset, annotate data, develop features, and report tagging results nearing 90% accuracy. The data and tools have been made available to the research community with the goal of enabling richer text analysis of Twitter and related social media data sets.

M3 - Conference contribution

SP - 42

EP - 47

BT - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics

PB - Association for Computational Linguistics

ER -

Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments

Abstract / Description of output

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