Room to Glo: A systematic comparison of semantic change detection approaches with word embeddings

Philippa Shoemark; Farhana Ferdousi Liza; Dong Nguyen; Scott A. Hale; Barbara McGillivray

Room to Glo: A systematic comparison of semantic change detection approaches with word embeddings

Philippa Shoemark
, Farhana Ferdousi Liza
, Dong Nguyen
, Scott A. Hale
, Barbara McGillivray

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

71 Citations (Scopus)

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.

Original language	English
Title of host publication	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
Publisher	Association for Computational Linguistics
Pages	66-76
Number of pages	11
ISBN (Electronic)	9781950737901
Publication status	Published - 2019
Event	2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019 - Hong Kong, China Duration: 3 Nov 2019 → 7 Nov 2019

Conference

Conference	2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019
Country/Territory	China
City	Hong Kong
Period	3/11/19 → 7/11/19

Cite this

Shoemark, P., Liza, F. F., Nguyen, D., Hale, S. A., & McGillivray, B. (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 (pp. 66-76). Association for Computational Linguistics.

Shoemark, Philippa ; Liza, Farhana Ferdousi ; Nguyen, Dong et al. / Room to Glo : A systematic comparison of semantic change detection approaches with word embeddings. 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, 2019. pp. 66-76

@inproceedings{3c171c3085bc400b8ec67d917d70a83f,

title = "Room to Glo: A systematic comparison of semantic change detection approaches with word embeddings",

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.",

author = "Philippa Shoemark and Liza, \{Farhana Ferdousi\} and Dong Nguyen and Hale, \{Scott A.\} and Barbara McGillivray",

note = "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: {\textcopyright} 2019 Association for Computational Linguistics; 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019 ; Conference date: 03-11-2019 Through 07-11-2019",

year = "2019",

language = "English",

pages = "66--76",

booktitle = "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",

publisher = "Association for Computational Linguistics",

}

Shoemark, P, Liza, FF, Nguyen, D, Hale, SA & McGillivray, B 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, pp. 66-76, 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, 3/11/19.

Room to Glo: A systematic comparison of semantic change detection approaches with word embeddings. / Shoemark, Philippa; Liza, Farhana Ferdousi; Nguyen, Dong et al.
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, 2019. p. 66-76.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Room to Glo

T2 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019

AU - Shoemark, Philippa

AU - Liza, Farhana Ferdousi

AU - Nguyen, Dong

AU - Hale, Scott A.

AU - McGillivray, Barbara

N1 - 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

PY - 2019

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N2 - 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.

AB - 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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M3 - Conference contribution

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SP - 66

EP - 76

BT - 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

PB - Association for Computational Linguistics

Y2 - 3 November 2019 through 7 November 2019

ER -

Shoemark P, Liza FF, Nguyen D, Hale SA, McGillivray B. 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. 2019. p. 66-76

Room to Glo: A systematic comparison of semantic change detection approaches with word embeddings

Abstract

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