@inproceedings{shoemark-etal-2019-room,
title = "Room to {G}lo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings",
author = "Shoemark, Philippa and
Liza, Farhana Ferdousi and
Nguyen, Dong and
Hale, Scott and
McGillivray, Barbara",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1007/",
doi = "10.18653/v1/D19-1007",
pages = "66--76",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings
%A Shoemark, Philippa
%A Liza, Farhana Ferdousi
%A Nguyen, Dong
%A Hale, Scott
%A McGillivray, Barbara
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F shoemark-etal-2019-room
%X 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.
%R 10.18653/v1/D19-1007
%U https://aclanthology.org/D19-1007/
%U https://doi.org/10.18653/v1/D19-1007
%P 66-76
Markdown (Informal)
[Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings](https://aclanthology.org/D19-1007/) (Shoemark et al., EMNLP-IJCNLP 2019)
ACL