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CEREC: A Corpus for Entity Resolution in Email Conversations

arXiv 2105.10606 ↗First posted 21 May 2021cs.CL cs.AIcited by 2

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Abstract (arXiv)

We present the first large scale corpus for entity resolution in email conversations (CEREC). The corpus consists of 6001 email threads from the Enron Email Corpus containing 36,448 email messages and 60,383 entity coreference chains. The annotation is carried out as a two-step process with minimal manual effort. Experiments are carried out for evaluating different features and performance of four baselines on the created corpus. For the task of mention identification and coreference resolution, a best performance of 59.2 F1 is reported, highlighting the room for improvement. An in-depth qualitative and quantitative error analysis is presented to understand the limitations of the baselines considered.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 5 of its 41 references that are in the map. First posted on arXiv 21 May 2021.

Builds on

5 of 41 references.

References · 5

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2018Deep contextualized word representationscited by 2,290
  3. 2019SpanBERT: Improving Pre-training by Representing and Predicting Spanscited by 834
  4. 2018Higher-order Coreference Resolution with Coarse-to-fine Inferencecited by 150
  5. 2019BERT for Coreference Resolution: Baselines and Analysiscited by 125

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Citing papers per year: 2025: 1; 2026: 1.

2 papers.