Stylianou, N., and Vlahavas, I., 2021. A neural Entity Coreference Resolution review. Expert Systems With Applications 168, p.114466.

Authors:

  • Nikolaos Stylianou, Ioannis Vlahavas

Keywords:

  • Coreference resolution, Neural Networks, Gender Bias, Pronoun resolution, Natural Language Processing, Discourse

Tags:

Abstract:

  • Entity Coreference Resolution is the task of resolving all mentions in a document that refer to the same real world entity and is considered as one of the most difficult tasks in natural language understanding. It is of great importance for downstream natural language processing tasks such as entity linking, machine translation, summarization, chatbots, etc. This work aims to give a detailed review of current progress on solving Coreference Resolution using neural-based approaches. It also provides a detailed appraisal of the datasets and evaluation metrics in the field, as well as the subtask of Pronoun Resolution that has seen various improvements in the recent years. We highlight the advantages and disadvantages of the approaches, the challenges of the task, the lack of agreed-upon standards in the task and propose a way to further expand the boundaries of the field.
Address

School of Informatics
Aristotle University of Thessaloniki
54124 Thessaloniki, Greece

Contact

Prof. Ioannis P. Vlahavas
tel: ++30 231099-8145
fax: ++30 231099-8362
e-mail: vlahavas@csd.auth.gr

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