ResToRinG CaPitaLiZaTion in #TweeTs | Zendy

Kamel Nebhi | Zendy; Kalina Bontcheva | Zendy; Genevieve Gorrell | Zendy

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ResToRinG CaPitaLiZaTion in #TweeTs

Author(s) -

Kamel Nebhi,

Kalina Bontcheva,

Genevieve Gorrell

Publication year - 2015

Publication title -

proceedings of the 24th international conference on world wide web

Language(s) - English

Resource type - Conference proceedings

DOI - 10.1145/2740908.2743039

Subject(s) - microblogging , computer science , social media , natural language processing , artificial intelligence , named entity recognition , capitalization , language model , information retrieval , world wide web , linguistics , task (project management) , engineering , philosophy , systems engineering

The rapid proliferation of microblogs such as Twitter has resulted in a vast quantity of written text becoming available that contains interesting information for NLP tasks. However, the noise level in tweets is so high that standard NLP tools perform poorly. In this pa- per, we present a statistical truecaser for tweets using a 3-gram language model built with truecased newswire texts and tweets. Our truecasing method shows an improvement in named entity recognition and part-of-speech tagging tasks.

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