Latent Variable Models of Selectional Preference

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Dependency-Based Word Embeddings (2014, methodology): “In some works, the dimensionality of the sparse word-context vectors is reduced, using techniques such as SVD (Bullinaria and Levy, 2007) or LDA (Ritter et al., 2010; Séaghdha, 2010; Cohen et al., 2012).”
Open domain event extraction from twitter (2012, methodology): “We adopt an approach based on latent variable models inspired by recent work on modeling selectional preferences [47, 39, 22, 52, 48], and unsupervised information extraction [4, 55, 7].”
A Bayesian Model of Diachronic Meaning Change (2016, background): “Bayesian models have been previously developed for various tasks in lexical semantics (Brody and La- pata, 2009; Ó Séaghdha, 2010; Ritter et al., 2010) and word meaning change detection is no exception.”
Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations (2016, methodology): “We consider both a factorization method inspired by previous research in knowledge base modeling (as discussed above) and another, even simpler one, based on ideas from previous research on modeling selectional preferences (e.g., Resnik (1997); Ó Séaghdha (2010); Van de Cruys (2010)), plus their…”