A framework for analyzing semantic change of words across time

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Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change (2016, methodology): “(e.g., Gulordava and Baroni, 2011; Jatowt and Duh, 2014) or by performing heuristic local alignments per word (Kulkarni et al.”
Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change (2019, background): “Previous evaluation procedures mainly tackle a few words: case studies of individual words (Wi-jaya and Yeniterzi, 2011; Jatowt and Duh, 2014; Hamilton et al., 2016a), or a comparison between a few changing and semantically stable words (Lau et al., 2012; Schlechtweg et al., 2017).”
Semantic Variation in Online Communities of Practice (2018, unknown): “A variety of techniques have been explored: Latent Semantic Analysis (Sagi et al., 2011; Jatowt and Duh, 2014), topic clustering (Wijaya and Yeniterzi, 2011) and dynamic topic modelling (Frermann and Lapata, 2016).”
Statistically Significant Detection of Linguistic Change (2014, methodology): “We created a reference data set D of 20 words that have been suggested by prior work [15, 17, 19, 39] as having undergone a linguistic change.”