Advances in Topic Models for Complex Document Network Data

Abstract

This paper reviews important advances that have been made in the past decade for topic modeling of large-scale document network data.Interest in topic modeling is worldwide and touches a number of practical text mining,computer vision and computational biology systems that are important in text summarization,information retrieval,information recommendation,topic detection and tracking,natural scene understanding,human motion categorization and microarray gene expression analysis.The main focus of this review is on the recent advances of topic modeling techniques for document network data.We introduce the four major characteristics of document network data and the current state-of-the-art topic models,with descriptions of what they are,what has been accomplished,and what remains to be done.Document network data contain dynamic,higher-order,multiplex,and distributed structures.Prior efforts on topic models focus on modeling parts of these structures for topic detection and tracking.To handle all document network structures,we discuss a three-dimensional Markov model that solves dynamic,higher-order,multiplex and distributed structures within a unified framework.In addition,we also discuss the integration of three-dimensional Markov models with type-2 fuzzy logic systems for distributed computing with words.Besides document network structure modeling,we also discuss the inference and parameter estimation method in terms of energy minimization for three-dimensional Markov models.