- Montanelli, S., & Periti, F. (2023). A Survey on Contextualised Semantic Shift Detection. arXiv preprint arXiv:2304.01666. [paper]
- Tahmasebi, N., Borin, L., & Jatowt, A. (2018). Survey of computational approaches to lexical semantic change. arXiv preprint arXiv:1811.06278. [paper]
-
Hamilton, W. L., Leskovec, J., & Jurafsky, D. (2016). Diachronic word embeddings reveal statistical laws of semantic change. arXiv preprint arXiv:1605.09096. [paper]
-
Bamler, R., & Mandt, S. (2017, July). Dynamic word embeddings. In International conference on Machine learning (pp. 380-389). PMLR. [paper]
-
Rudolph, M., & Blei, D. (2017). Dynamic bernoulli embeddings for language evolution. arXiv preprint arXiv:1703.08052. [paper] [paper_review]
-
Yao, Z., Sun, Y., Ding, W., Rao, N., & Xiong, H. (2018, February). Dynamic word embeddings for evolving semantic discovery. In Proceedings of the eleventh acm international conference on web search and data mining (pp. 673-681). [paper] :: [paper_review]
-
Ethayarajh, K. (2019). How contextual are contextualized word representations? Comparing the geometry of BERT, ELMo, and GPT-2 embeddings. arXiv preprint arXiv:1909.00512. [paper]
-
Di Carlo, V., Bianchi, F., & Palmonari, M. (2019, July). Training temporal word embeddings with a compass. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, No. 01, pp. 6326-6334). [paper]
-
Gong, H., Bhat, S., & Viswanath, P. (2020). Enriching word embeddings with temporal and spatial information. arXiv preprint arXiv:2010.00761. [paper]
-
Hofmann, V., Pierrehumbert, J. B., & Schütze, H. (2020). Dynamic contextualized word embeddings. arXiv preprint arXiv:2010.12684. [paper] :: [ paper_review ]