This paper describes the application of so-called topic models to selectional preference induction. Three models related to Latent Dirichlet Allocation, a proven method for modelling document-word cooccurrences, are presented and evaluated on datasets of human plausibility judgements. Compared to previously proposed techniques, these models perform very competitively, especially for infrequent ...

We propose models for semantic orientations of phrases as well as classification methods based on the models. Although each phrase consists of multiple words, the semantic orientation of the phrase is not a mere sum of the orientations of the component words. Some words can invert the orientation. In order to capture the property of such phrases, we introduce latent variables into the models. T...

Latent class and the Grade of Membership (GoM) models are two examples of latent structure models. Latent class models are discrete mixture models. The GoM model has been originally developed as an extension of latent class models to a continuous mixture. This note describes a constrained latent class model which is equivalent to the GoM model, and provides a detailed proof of this equivalence....

The data in this article supports the research paper entitled "Interexaminer variation of minutia markup on latent fingerprints" [1]. The data in this article describes the variability in minutia markup during both analysis of the latents and comparison between latents and exemplars. The data was collected in the "White Box Latent Print Examiner Study," in which each of 170 volunteer latent pri...

We highlight key differences between the present work and an interpretable, latent state model introduced by (Lian et al., 2014). In it, the model has one sequence of latent (K binary events) states (e.g., progression in a movie); each user experiences the same sequence of latent states but may react differently, resulting in a user-specific intensity function that produces a response to the la...

This article discusses a latent variable model for inference and prediction of symmetric relational data. The model, based on the idea of the eigenvalue decomposition, represents the relationship between two nodes as the weighted inner-product of node-specific vectors of latent characteristics. This “eigenmodel” generalizes other popular latent variable models, such as latent class and distance...

Latent variable models exist with continuous, categorical, or both types of latent variables. The role of latent variables is to account for systematic patterns in the observed responses. This article has two goals: (a) to establish whether, based on observed responses, it can be decided that an underlying latent variable is continuous or categorical, and (b) to quantify the effect of sample si...

The stable latent reservoir for HIV-1 in resting memory CD4 T cells is a major barrier to curing HIV-1/AIDS. Studies on HIV-1 latency focused on regulation and reactivation of viral gene expression in the cells in which viral latent infection is already established. However, it is not well understood how virus becomes latent. In this study, we found that in a subset of memory precursor CD4 T ce...

Summary Usually in latent class (LC) analysis, external predictors are taken to be cluster conditional probability (LC models with predictors), and/or score regression models). In such cases, their distribution is not of interest. Class‐specific interest the distal outcome model, when variables assumed depend on LC membership. this paper, we consider a more general formulation, that embeds both...