Paper: Adapting taggers to Twitter with not-so-distant supervision

ACL ID C14-1168
Title Adapting taggers to Twitter with not-so-distant supervision
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

We experiment with using different sources of distant supervision to guide unsupervised and semi-supervised adaptation of part-of-speech (POS) and named entity taggers (NER) to Twitter. We show that a particularly good source of not-so-distant supervision is linked websites. Specif- ically, with this source of supervision we are able to improve over the state-of-the-art for Twitter POS tagging (89.76% accuracy, 8% error reduction) and NER (F1=79.4%, 10% error reduction).