Review published In:
Journal of Historical LinguisticsVol. 14:2 (2024) ► pp.376–384
Book review
. Computational Approaches to Semantic Change [= Language Variation, 6]. Berlin: Language Science Press, 2021. https://doi.org/10.5281/zenodo.5040241
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Available under the Creative Commons Attribution (CC BY) 4.0 license.
For any use beyond this license, please contact the publisher at [email protected].
References (15)
Chen, X., L. Xu, Z. Liu, M. Sun & H. -B. Luan. 2015. Joint Learning of Character and Word Embeddings. Proceedings of IJCAI 2015, 1236–1242. Cambridge, MA: AAAI Press.
Davies, M. 2008. The Corpus of Contemporary American English (COCA): 560 Million Words, 1990–Present.
2012. Expanding Horizons in Historical Linguistics with the 400-Million Word Corpus of Historical American English. Corpora 7:2.121–157. 
Devlin, J., M. -W. Chang, K. Lee & K. Toutanova. 2019. BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) ed. by Jill Burstein, Christy Doran & Thamar Solorio, 4171–4186. Minneapolis, MN: Association for Computational Linguistics.
Frermann, L. & M. Lapata. 2016. A Bayesian Model of Diachronic Meaning Change. Transactions of the ACL 41:31–45.
Hamilton, W. L., J. Leskovec & D. Jurafsky. 2016. Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) ed. by Katrin Erk & Noah A. Smith, 1489–1501. Berlin: Association for Computational Linguistics.
Haslam, N. 2016. Concept Creep: Psychology’s Expanding Concepts of Harm and Pathology. Psychological Inquiry 27:1.1–17.
Joulin, A., E. Grave, P. Bojanowski, M. Douze, H. Jégou & T. Mikolov, T. 2016. Fasttext.zip: Compressing Text Classification Models. arXiv preprint arXiv:1612.03651.
McGillivray, B. & A. Kilgarriff. 2013. Tools for Historical Corpus Research and a Corpus of Latin. New Methods in Historical Corpus Linguistics ed. by P. Bennett, M. Durrell, S. Scheible & R. J. Whitt, 247–257. Tübingen: Narr.
Michel, J. -B., Y. K. Shen, A. P. Aiden, A. Veres, M. K. Gray, The Google Books Team, J. P. Pickett, D. Hoiberg, D. Clancy, P. Norvig, J. Orwant, S. Pinker, M. A. Nowak & E. L. Aiden. 2011. Quantitative Analysis of Culture Using Millions of Digitized Books. Science 331:6014.176–182.
Mikolov, T., I. Sutskever, K. Chen, G. Corrado & J. Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems 261:3111–3119.
Turney, P. D. & P. Pantel. 2010. From Frequency to Meaning: Vector Space Models of Semantics. Journal of Artificial Intelligence Research 37:1.141–188.
Vatri, A. & B. McGillivray. 2018. The Diorisis Ancient Greek Corpus. Research Data Journal for the Humanities and Social Sciences 3:1.55–65.
Wiedemann, G., S. Remus, A. Chawla & C. Biemann. 2019. Does BERT Make any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings. In Proceedings of KONVENS 2019. Erlangen, Germany, October 9–11.
Yao, Z., Y. Sun, W. Ding, N. Rao & H. Xiong. 2018. Dynamic Word Embeddings for Evolving Semantic Discovery. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 673–681. Marina Del Rey, CA, February 5–9.