Work

Arabic cross-document coreference detection

Year: 2009

Type: conference-paper

Abstract: We describe a set of techniques for Arabic cross-document coreference resolution. We compare a baseline system of exact mention string-matching to ones that include local mention context information as well as information from an existing machine translation system. It turns out that the machine translation-based technique outperforms the baseline, but local entity context similarity does not. This helps to point the way for future cross-document coreference work in languages with few existing resources for the task.

Source: Joint Conference of the Annual Meeting of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing (ACL-IJCNLP)

DOI: 10.3115/1667583.1667694

Authors: Asad Sayeed, Tamer Elsayed, Nikesh Garera, David Alexander, Tan Xu, Douglas W. Oard, David Yarowsky, Christine Piatko

Institutions: Johns Hopkins University, University of Maryland, College Park

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W1965632217

FWCI: 0.234

Cites: 9

Cited by: 3

Related to: 10