A brief survey on recent advances in coreference resolution

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.

Automated summary

The task of resolving repeated objects in natural languages, known as coreference resolution, is an important part of modern natural language processing. It is classified into entity coreference resolution and event coreference resolution based on the resolved objects. Predicting coreference connections and identifying mentions/triggers are the major challenges in coreference resolution due to the difficulty of implicit relationships in natural language understanding. In this survey, we review the current employed evaluation metrics, datasets, and methods, investigating 10 widely used metrics, 18 datasets, and 4 main technical trends. We believe that this work provides a comprehensive roadmap for understanding the past and the future of coreference resolution.