Adapting taggers to Twitter with not-so-distant supervision

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).