Latent Dependency Forest Models

2016/09/08 by Shanbo Chu, Yong Jiang, Chu, Shanbo +3
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Natural Language Processing Techniques #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1609.02236

openalex publication_date 2016/09/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Probabilistic modeling is one of the foundations of modern machine learning and artificial intelligence. In this paper, we propose a novel type of probabilistic models named latent dependency forest models (LDFMs). A LDFM models the dependencies between random variables with a forest structure that can change dynamically based on the variable values. It is therefore capable of modeling context-specific independence. We parameterize a LDFM using a first-order non-projective dependency grammar. Learning LDFMs from data can be formulated purely as a parameter learning problem, and hence the difficult problem of model structure learning is circumvented. Our experimental results show that LDFMs are competitive with existing probabilistic models.

Citations

• Sum-Product Networks: A New Deep Architecture

Related

• DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures
• Learning Latent Trees with Stochastic Perturbations and Differentiable Dynamic Programming
• Probabilistic Dependency Graphs
• Latent Variable Discovery Using Dependency Patterns
• Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective