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 F 1 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 F 1 by 25% over ten common entity types. Our NLP tools are available at: http:// github.com/aritter/twitter_nlp
In REF , the authors reported that the performance of standard NLP tools severely degrade on tweets.
Named Entity Recognition in Tweets: An Experimental Study
EMNLP
Computer Science