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A brief survey on recent advances in coreference resolution

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Abstract

The task of resolving repeated objects in natural languages is known as coreference resolution, and it is an important part of modern natural language processing. It is classified into two categories depending on the resolved objects, namely entity coreference resolution and event coreference resolution. Predicting coreference connections and identifying mentions/triggers are the major challenges in coreference resolution, because these implicit relationships are particularly difficult in natural language understanding in downstream tasks. Coreference resolution techniques have experienced considerable advances in recent years, encouraging us to review this task in the following aspects: current employed evaluation metrics, datasets, and methods. We investigate 10 widely used metrics, 18 datasets and 4 main technical trends in this survey. We believe that this work is a comprehensive roadmap for understanding the past and the future of coreference resolution.

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Notes

  1. either as entity mentions or event mentions.

  2. https://github.com/huggingface/neuralcoref.

  3. https://www.media.mit.edu/projects/open-mind-common-sense/overview/.

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Acknowledgements

This study is supported under the RIE2020 Industry Alignment Fund-Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s).

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  1. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Ruicheng Liu, Rui Mao, Anh Tuan Luu & Erik Cambria

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Liu, R., Mao, R., Luu, A.T. et al. A brief survey on recent advances in coreference resolution. Artif Intell Rev 56, 14439–14481 (2023). https://doi.org/10.1007/s10462-023-10506-3

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