The Challenges of Cross-Document Coreference Resolution for Email

Xue Li, Sara Magliacane, Paul Groth

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Long-form conversations such as email are an important source of information for knowledge capture. For tasks such as knowledge graph construction, conversational search, and entity linking, being able to resolve entities from across documents is important. Building on recent work on within document coreference resolution for email, we study for the first time a cross-document formulation of the problem. Our results show that the current state-of-the-art deep learning models for general cross-document coreference resolution are insufficient for email conversations. Our experiments show that the general task is challenging and, importantly for knowledge intensive tasks, coreference resolution models that only treat entity mentions perform worse. Based on these results, we outline the work needed to address this challenging task.

Original languageEnglish
Title of host publicationK-CAP 2021 - Proceedings of the 11th Knowledge Capture Conference
PublisherAssociation for Computing Machinery, Inc
Pages273-276
Number of pages4
ISBN (Electronic)9781450384575
DOIs
StatePublished - Dec 2 2021
Event11th ACM International Conference on Knowledge Capture, K-CAP 2021 - Virtual, Online, United States
Duration: Dec 2 2021Dec 3 2021

Publication series

NameK-CAP 2021 - Proceedings of the 11th Knowledge Capture Conference

Conference

Conference11th ACM International Conference on Knowledge Capture, K-CAP 2021
Country/TerritoryUnited States
CityVirtual, Online
Period12/2/2112/3/21

Keywords

  • challenges
  • conversational data
  • cross-document coreference resolution
  • email conversations
  • entity resolution

Fingerprint

Dive into the research topics of 'The Challenges of Cross-Document Coreference Resolution for Email'. Together they form a unique fingerprint.

Cite this