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Shoemark, Philippa, Liza, Farhana Ferdousi 
ORCID: https://orcid.org/0000-0003-4854-5619, Nguyen, Dong, Hale, Scott A. and McGillivray, Barbara
(2019)
Room to Glo:A systematic comparison of semantic change detection approaches with word embeddings.
In:
EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference.
Association for Computational Linguistics, CHN, pp. 66-76.
ISBN 9781950737901
Abstract
Word embeddings are increasingly used for the automatic detection of semantic change; yet, a robust evaluation and systematic comparison of the choices involved has been lacking. We propose a new evaluation framework for semantic change detection and find that (i) using the whole time series is preferable over only comparing between the first and last time points; (ii) independently trained and aligned embeddings perform better than continuously trained embeddings for long time periods; and (iii) that the reference point for comparison matters. We also present an analysis of the changes detected on a large Twitter dataset spanning 5.5 years.
| Item Type: | Book Section |
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| Additional Information: | Funding Information: This work was supported by The Alan Tur ing Institute under the UK Engineering and Physical Sciences Research Council (EPSRC) grant EP/N510129/1. P.S. was supported in part by the EPSRC Centre for Doctoral Training in Data Science, funded by the UK EP-SRC (grant EP/L016427/1) and the University of Edinburgh. D.N. was supported by Turing award TU/A/000006 and B.McG. by Turing award TU/A/000010 (RG88751). S.A.H. was supported in part by The Volkswagen Foundation. Funding Information: This work was supported by The Alan Turing Institute under the UK Engineering and Physical Sciences Research Council (EPSRC) grant EP/N510129/1. P.S. was supported in part by the EPSRC Centre for Doctoral Training in Data Science, funded by the UK EPSRC (grant EP/L016427/1) and the University of Edinburgh. D.N. was supported by Turing award TU/A/000006 and B.McG. by Turing award TU/A/000010 (RG88751). S.A.H. was supported in part by The Volkswagen Foundation. Publisher Copyright: © 2019 Association for Computational Linguistics |
| Uncontrolled Keywords: | computational theory and mathematics,computer science applications,information systems ,/dk/atira/pure/subjectarea/asjc/1700/1703 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 26 Sep 2024 16:30 |
| Last Modified: | 30 Sep 2024 12:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/96823 |
| DOI: |
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