Machine Learning for Entity Coreference Resolution: A Retrospective Look at Two Decades of Research

Authors

  • Vincent Ng University of Texas at Dallas

DOI:

https://doi.org/10.1609/aaai.v31i1.11149

Keywords:

natural language processing, text mining, coreference resolution

Abstract

Though extensively investigated since the 1960s, entity coreference resolution, a core task in natural language understanding, is far from being solved. Nevertheless, significant progress has been made on learning-based coreference research since its inception two decades ago. This paper provides an overview of the major milestones made in learning-based coreference research and discusses a hard entity coreference task, the Winograd Schema Challenge, which has recently received a lot of attention in the AI community.

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Published

2017-02-12

How to Cite

Ng, V. (2017). Machine Learning for Entity Coreference Resolution: A Retrospective Look at Two Decades of Research. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.11149