Year: 2016
Type: conference-paper
Abstract: There is compelling evidence that coreference prediction would benefit from modeling global information about entity-clusters.Yet, state-of-the-art performance can be achieved with systems treating each mention prediction independently, which we attribute to the inherent difficulty of crafting informative clusterlevel features.We instead propose to use recurrent neural networks (RNNs) to learn latent, global representations of entity clusters directly from their mentions.We show that such representations are especially useful for the prediction of pronominal mentions, and can be incorporated into an end-to-end coreference system that outperforms the state of the art without requiring any additional search.
DOI: 10.18653/v1/n16-1114
Institution: Harvard University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2963695529