Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings
By Philippa Shoemark, Farhana Ferdousi Liza, Dong Nguyen, Scott A. Hale, Barbara McGillivray. 2019
95 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Cultural Studies, General Social Sciences
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Citations in the corpus
- Continual Lifelong Learning in Natural Language Processing: A Survey (2020, background): “There is an established consensus that the meaning of words changes over time due to complicated linguistic and social processes (e.g. Kutuzov et al., 2018; Shoemark et al., 2019).”
- Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation (2020, unknown): “2Throughout this paper, we define the semantics of words to be the meanings and functionality of words other than the gender information by following Shoemark et al. (2019).”