Work

Conundrums in Event Coreference Resolution: Making Sense of the State of the Art

Year: 2021

Type: conference-paper

Abstract: Despite recent promising results achieved by span-based approaches to event coreference resolution, there is a lack of understanding of what has been improved.We present an empirical analysis of our state-of-the-art span-based event coreference resolver (Lu and Ng, 2021) with the goal of providing the general NLP audience with a better understanding of the state of the art and coreference researchers with directions for future research.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/2021.emnlp-main.103

Authors: Jing Lu, Vincent Ng

Institution: The University of Texas at Dallas

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Science Foundation

Award: RI: Small: Fast, Scalable Joint Inference for NLP using Markov Logic