Work

Unsupervised Ranking Model for Entity Coreference Resolution

Year: 2016

Type: conference-paper

Abstract: Coreference resolution is one of the first stages in deep language understanding and its importance has been well recognized in the natural language processing community. In this paper, we propose a generative, unsupervised ranking model for entity coreference resolution by introducing resolution mode variables. Our unsupervised system achieves 58.44% F1 score of the CoNLL metric on the English data from the CoNLL-2012 shared task (Pradhan et al., 2012), outperforming the Stanford deterministic system (Lee et al., 2013) by 3.01%.

Source: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)

DOI: 10.18653/v1/n16-1116

Authors: Xuezhe Ma, Zhengzhong Liu, Eduard Hovy

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Defense Advanced Research Projects Agency

Awards: G4713059963, G5432064702

OpenAlex ID: W2963254740

FWCI: 1.634

Cites: 35

Cited by: 18

Related to: 10