CDˆ2CR: Co-reference resolution across documents and domains

James Ravenscroft, Amanda Clare, Arie Cattan, Ido Dagan, Maria Liakata

Abstract
Cross-document co-reference resolution (CDCR) is the task of identifying and linking mentions to entities and concepts across many text documents. Current state-of-the-art models for this task assume that all documents are of the same type (e.g. news articles) or fall under the same theme. However, it is also desirable to perform CDCR across different domains (type or theme). A particular use case we focus on in this paper is the resolution of entities mentioned across scientific work and newspaper articles that discuss them. Identifying the same entities and corresponding concepts in both scientific articles and news can help scientists understand how their work is represented in mainstream media. We propose a new task and English language dataset for cross-document cross-domain co-reference resolution (CDˆ2CR). The task aims to identify links between entities across heterogeneous document types. We show that in this cross-domain, cross-document setting, existing CDCR models do not perform well and we provide a baseline model that outperforms current state-of-the-art CDCR models on CDˆ2CR. Our data set, annotation tool and guidelines as well as our model for cross-document cross-domain co-reference are all supplied as open access open source resources.
Anthology ID:
2021.eacl-main.21
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
270–280
Language:
URL:
https://aclanthology.org/2021.eacl-main.21
DOI:
10.18653/v1/2021.eacl-main.21
Bibkey:
Cite (ACL):
James Ravenscroft, Amanda Clare, Arie Cattan, Ido Dagan, and Maria Liakata. 2021. CDˆ2CR: Co-reference resolution across documents and domains. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 270–280, Online. Association for Computational Linguistics.
Cite (Informal):
CDˆ2CR: Co-reference resolution across documents and domains (Ravenscroft et al., EACL 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/auto-file-uploads/2021.eacl-main.21.pdf
Data
ECB+
PDF Search