Adapting taggers to Twitter with not-so-distant supervision

TY - GEN

T1 - Adapting taggers to Twitter with not-so-distant supervision

AU - Plank, Barbara

AU - Hovy, Dirk

AU - McDonald, Ryan

AU - Søgaard, Anders

PY - 2014

Y1 - 2014

N2 - 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. Specifically, 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).

AB - 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. Specifically, 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).

M3 - Article in proceedings

SP - 1783

EP - 1792

BT - Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

PB - Association for Computational Linguistics

CY - Dublin, Ireland

ER -