Machine Learning for Entity Coreference Resolution: A Retrospective Look at Two Decades of Research
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.
Cite
Text
Ng. "Machine Learning for Entity Coreference Resolution: A Retrospective Look at Two Decades of Research." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.11149Markdown
[Ng. "Machine Learning for Entity Coreference Resolution: A Retrospective Look at Two Decades of Research." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/ng2017aaai-machine/) doi:10.1609/AAAI.V31I1.11149BibTeX
@inproceedings{ng2017aaai-machine,
title = {{Machine Learning for Entity Coreference Resolution: A Retrospective Look at Two Decades of Research}},
author = {Ng, Vincent},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2017},
pages = {4877-4884},
doi = {10.1609/AAAI.V31I1.11149},
url = {https://mlanthology.org/aaai/2017/ng2017aaai-machine/}
}