The Challenges of Cross-Document Coreference Resolution in Email
| Authors | |
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| 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) |
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| 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 |
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| 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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