Year: 2020
Type: conference-paper
Abstract: Coreference resolution (CR) is an essential part of discourse analysis.Most recently, neural approaches have been proposed to improve over SOTA models from earlier paradigms.So far none of the published neural models leverage external semantic knowledge such as type information.This paper offers the first such model and evaluation, demonstrating modest gains in accuracy by introducing either gold standard or predicted types.In the proposed approach, type information serves both to (1) improve mention representation and (2) create a soft type consistency check between coreference candidate mentions.Our evaluation covers two different grain sizes of types over four different benchmark corpora.
Authors: Sopan Khosla, Carolyn Rose
Institution: Carnegie Mellon University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W3093459127