Singleton detection using word embeddings and neural networks

Abstract

Singleton (or non-coreferential) mentions are a problem for coreference resolution systems, and identifying singletons before mentions are linked improves resolution performance. Here, a singleton detection system based on word embeddings and neural networks is presented, which achieves state-of-The-Art performance (79.6% accuracy) on the CoNLL- 2012 shared task development set. Extrinsic evaluation with the Stanford and Berkeley coreference resolution systems shows significant improvement for the first, but not for the latter. The results show the potential of using neural networks and word embeddings for improving both singleton detection and coreference resolution.