Year: 2015
Type: conference-paper
Abstract: Coreference resolution is a key problem in natural language understanding that still escapes reliable solutions.One fundamental difficulty has been that of resolving instances involving pronouns since they often require deep language understanding and use of background knowledge.In this paper we propose an algorithmic solution that involves a new representation for the knowledge required to address hard coreference problems, along with a constrained optimization framework that uses this knowledge in coreference decision making.Our representation, Predicate Schemas, is instantiated with knowledge acquired in an unsupervised way, and is compiled automatically into constraints that impact the coreference decision.We present a general coreference resolution system that significantly improves state-of-the-art performance on hard, Winograd-style, pronoun resolution cases, while still performing at the stateof-the-art level on standard coreference resolution datasets.
DOI: 10.3115/v1/n15-1082
Authors: Haoruo Peng, Daniel Khashabi, Dan Roth
Institution: University of Illinois Urbana-Champaign
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G389929128, G4713059963
OpenAlex ID: W2296266385