The Challenges of Cross-Document Coreference Resolution in Email

被引:1
|
作者
Li, Xue [1 ]
Magliacane, Sara [2 ]
Groth, Paul [1 ]
机构
[1] Univ Amsterdam, Amsterdam, Netherlands
[2] Univ Amsterdam, MIT IBM Watson AI Lab, Amsterdam, Netherlands
来源
PROCEEDINGS OF THE 11TH KNOWLEDGE CAPTURE CONFERENCE (K-CAP '21) | 2021年
基金
荷兰研究理事会;
关键词
cross-document coreference resolution; email conversations; entity resolution; challenges; conversational data;
D O I
10.1145/3460210.3493573
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:273 / 276
页数:4
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