TY - GEN
T1 - The Challenges of Cross-Document Coreference Resolution for Email
AU - Li, Xue
AU - Magliacane, Sara
AU - Groth, Paul
N1 - Publisher Copyright:
© 2021 Owner/Author.
PY - 2021/12/2
Y1 - 2021/12/2
N2 - 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.
AB - 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.
KW - challenges
KW - conversational data
KW - cross-document coreference resolution
KW - email conversations
KW - entity resolution
UR - http://www.scopus.com/inward/record.url?scp=85120856582&partnerID=8YFLogxK
U2 - 10.1145/3460210.3493573
DO - 10.1145/3460210.3493573
M3 - Contribución a la conferencia
AN - SCOPUS:85120856582
T3 - K-CAP 2021 - Proceedings of the 11th Knowledge Capture Conference
SP - 273
EP - 276
BT - K-CAP 2021 - Proceedings of the 11th Knowledge Capture Conference
PB - Association for Computing Machinery, Inc
T2 - 11th ACM International Conference on Knowledge Capture, K-CAP 2021
Y2 - 2 December 2021 through 3 December 2021
ER -