Using Type Information to Improve Entity Coreference Resolution

10/12/2020
by   Sopan Khosla, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/20/2021

Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance

Recent work on entity coreference resolution (CR) follows current trends...
research
06/02/2023

Light Coreference Resolution for Russian with Hierarchical Discourse Features

Coreference resolution is the task of identifying and grouping mentions ...
research
06/02/2021

Cross-document Coreference Resolution over Predicted Mentions

Coreference resolution has been mostly investigated within a single docu...
research
12/08/2016

Entity Identification as Multitasking

Standard approaches in entity identification hard-code boundary detectio...
research
08/22/2022

Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing

Fine-grained entity typing (FET) aims to deduce specific semantic types ...
research
01/05/2023

Anaphora Resolution in Dialogue: System Description (CODI-CRAC 2022 Shared Task)

We describe three models submitted for the CODI-CRAC 2022 shared task. T...

Please sign up or login with your details

Forgot password? Click here to reset