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

Vincent Ng.
Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp. 4877-4884, 2017.

Click here for the PostScript or PDF version. The talk slides are available here.

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 learningbased 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.

BibTeX entry

@InProceedings{Ng:17a,
  author = {Vincent Ng},
  title = {Machine Learning for Entity Coreference Resolution: A Retrospective Look at Two Decades of Research},
  booktitle = {Proceedings of the 31st AAAI Conference on Artificial Intelligence},
  pages = {4877--4884}, 
  year = 2017}