Hierarchical semi-Markov conditional random fields for deep recursive sequential data

Truyen Tran, Dinh Phung, Hung Bui, Svetha Venkatesh

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)


We present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of linear-chain conditional random fields to model deep nested Markov processes. It is parameterised as a conditional log-linear model and has polynomial time algorithms for learning and inference. We derive algorithms for partially-supervised learning and constrained inference. We develop numerical scaling procedures that handle the overflow problem. We show that when depth is two, the HSCRF can be reduced to the semi-Markov conditional random fields. Finally, we demonstrate the HSCRF on two applications: (i) recognising human activities of daily living (ADLs) from indoor surveillance cameras, and (ii) noun-phrase chunking. The HSCRF is capable of learning rich hierarchical models with reasonable accuracy in both fully and partially observed data cases.

Original languageEnglish
Pages (from-to)53-85
Number of pages33
JournalArtificial Intelligence
Publication statusPublished - May 2017
Externally publishedYes


  • Constrained inference
  • Deep nested sequential processes
  • Hierarchical semi-Markov conditional random field
  • Numerical scaling
  • Partial labelling

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