Paper: Joint Learning for Coreference Resolution with Markov Logic

ACL ID D12-1114
Title Joint Learning for Coreference Resolution with Markov Logic
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012

Pairwise coreference resolution models must merge pairwise coreference decisions to gen- erate final outputs. Traditional merging meth- ods adopt different strategies such as the best- first method and enforcing the transitivity con- straint, but most of these methods are used independently of the pairwise learning meth- ods as an isolated inference procedure at the end. We propose a joint learning model which combines pairwise classification and mention clustering with Markov logic. Experimen- tal results show that our joint learning sys- tem outperforms independent learning sys- tems. Our system gives a better performance than all the learning-based systems from the CoNLL-2011 shared task on the same dataset. Compared with the best system from CoNLL- 2011, which employs a rule-based meth...