Paper: Unsupervised Coreference Resolution in a Nonparametric Bayesian Model

ACL ID P07-1107
Title Unsupervised Coreference Resolution in a Nonparametric Bayesian Model
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007
Authors

We present an unsupervised, nonparamet- ric Bayesian approach to coreference reso- lution which models both global entity iden- tity across a corpus as well as the sequen- tial anaphoric structure within each docu- ment. While most existing coreference work is driven by pairwise decisions, our model is fully generative, producing each mention from a combination of global entity proper- ties and local attentional state. Despite be- ingunsupervised,oursystemachievesa70.3 MUC F1 measure on the MUC-6 test set, broadly in the range of some recent super- vised results.