Moosavi, N.S. orcid.org/0000-0002-8332-307X and Strube, M. (2014) Unsupervised coreference resolution by utilizing the most informative relations. In: Tsujii,, J. and Hajic, J., (eds.) Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. 25th International Conference on Computational Linguistics (COLING 2014), 23-29 Aug 2014, Dublin, Ireland. Dublin City University and Association for Computational Linguistics , pp. 644-655. ISBN 9781941643266
Abstract
In this paper we present a novel method for unsupervised coreference resolution. We introduce a precision-oriented inference method that scores a candidate entity of a mention based on the most informative mention pair relation between the given mention entity pair. We introduce an informativeness score for determining the most precise relation of a mention entity pair regarding the coreference decisions. The informativeness score is learned robustly during few iterations of the expectation maximization algorithm. The proposed unsupervised system outperforms existing unsupervised methods on all benchmark data sets.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2014 Association for Computational Linguistics (ACL). Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License (https://creativecommons.org/licenses/by-nc-sa/3.0/). Permission is granted to make copies for the purposes of teaching and research. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 07 Jun 2023 10:30 |
Last Modified: | 07 Jun 2023 10:30 |
Published Version: | https://aclanthology.org/C14-1061 |
Status: | Published |
Publisher: | Dublin City University and Association for Computational Linguistics |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:199939 |