The Challenges of Cross-Document Coreference Resolution in Email

Open Access
Authors
Publication date 2021
Book title K-CAP '21
Book subtitle Proceedings of the 11th Knowledge Capture Conference : December 2-3, 2021 : virtual event, USA
ISBN (electronic)
  • 9781450384575
Event 11th ACM International Conference on Knowledge Capture, K-CAP 2021
Pages (from-to) 273-276
Number of pages 4
Publisher New York, NY: Association for Computing Machinery
Organisations
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
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.

Document type Conference contribution
Note Funding for this research comes from the Dutch Research Council (NWO) through grant MVI.19.032.
Language English
Published at https://doi.org/10.1145/3460210.3493573
Other links https://www.scopus.com/pages/publications/85120856582
Downloads
3460210.3493573 (Final published version)
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