Precision Event Coreference Resolution Using Neural Network Classifiers

Authors

  • Arun Pandian Carnegie Mellon University in Qatar
  • Lamana Mulaffer Carnegie Mellon University in Qatar
  • Kemal Oflazer Carnegie Mellon University in Qatar
  • Amna AlZeyara Qatar University

DOI:

https://doi.org/10.13053/cys-1-1-3349

Keywords:

Deep learning, event coreference, semantics

Abstract

This paper presents a neural network classifier approach to detecting precise within-document (WD) and cross-document (CD) event coreference clusters effectively using only event mention based features. Our approach does not rely on any event argument features such as semantic roles or spatio-temporal arguments and uses no sophisticated clustering approach. Experimental results on the ECB+dataset show that our simple approach out performsstate-of-the-art methods for both within-document and cross-document event coreference resolution while producing clusters of high precision, which is useful for several downstream tasks.

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Published

2020-03-25

Issue

Vol. 24 No. 1 (2020): 24(1)2020

Section

Articles

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