Learning Global Features for Coreference Resolution

NAACL 2016 Sam WisemanAlexander M. RushStuart M. Shieber

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 cluster-level features... (read more)

PDF Abstract

Evaluation Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
Coreference Resolution OntoNotes Global F1 64.21 # 14