A Continuous-Time Model of Topic Co-occurrence Trends

Abstract

Recent work in statistical topic models has investigated richer structures to capture either temporal or inter-topic correlations. This paper introduces a topic model that combines the advantages of two recently proposed models: (1) The Pachinko Allocation model (PAM), which captures arbitrary topic correlations with a directed acyclic graph (DAG), and (2) the Topics over Time model (TOT), which captures time-localized shifts in topic prevalence with a continuous distribution over time stamps. Our model can thus capture not only temporal patterns in individual topics, but also the temporal patterns in their co-occurrences. We present results on a research paper corpus, showing interesting correlations among topics and their changes over time.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA449612

Entities

People

  • Andrew McCallum
  • Wei Li
  • Xuerui Wang

Organizations

  • University of Massachusetts Amherst

Tags

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  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence Software
  • Automata Theory
  • Automated Speech Recognition
  • Computational Science
  • Computer Languages
  • Computer Science
  • Data Sets
  • Information Science
  • Language
  • Machine Learning
  • Markov Models
  • Natural Language Processing
  • Neural Networks
  • Ontologies
  • Probability
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Computational Modeling and Simulation
  • Graph Algorithms and Convex Optimization.