This paper presents an evaluation method employing a latent variable model for paraphrases with their contexts. We assume that the context of a sentence is indicated by a latent variable of the model as a topic and that the likelihood of each variable can be inferred. A paraphrase is evaluated for whether its sentences are used in the same context. Experimental results showed that the proposed ...

This paper presents a Bayesian latent variable model used to analyze ordinal response survey data by taking into account the characteristics of respondents. The ordinal response data are viewed as multivariate responses arising from continuous latent variables with known cut-points. Each respondent is characterized by two parameters that have a Dirichlet process as their joint prior distributio...

For simplicity we will focus on a simple Gaussian Mixture Model. Consider a mixture of k spherical gaussians in R which is the following generative process. Let w ∈ ∆([k]) denote a distribution and let μ1, . . . , μk ∈ R be the mean vectors. Each point xi is generated by first choosing a component hi ∼ w and then xi ∼ N (μhi , I). We are given n samples x1, . . . , xn drawn according to this pr...

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...

We investigate a nonparametric model with which to visualize the relationship between two datasets. We base our model on Gaussian Process Latent Variable Models (GPLVM)[1],[2], a probabilistically defined latent variable model which takes the alternative approach of marginalizing the parameters and optimizing the latent variables; we optimize a latent variable set for each dataset, which preser...

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...

In this paper a nonparametric latent variable model is estimated without specifying the underlying distributions. The main idea is to estimate in a first step a common factor analysis model under the assumption that each manifest variable is influenced by at most one of the latent variables. In a second step nonparametric regression is used to analyze the relation between the latent variables. ...