Topic modeling with nonparametric Markov tree

Haojun Chen; David B. Dunson; Lawrence Carin

Topic modeling with nonparametric Markov tree

Haojun Chen, David B. Dunson, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A new hierarchical tree-based topic model is developed, based on nonparametric Bayesian techniques. The model has two unique attributes: (i) a child node in the tree may have more than one parent, with the goal of eliminating redundant sub-topics deep in the tree; and (ii) parsimonious sub-topics are manifested, by removing redundant usage of words at multiple scales. The depth and width of the tree are unbounded within the prior, with a retrospective sampler employed to adaptively infer the appropriate tree size based upon the corpus under study. Excellent quantitative results are manifested on five standard data sets, and the inferred tree structure is also found to be highly interpretable. Copyright 2011 by the author(s)/owner(s).

Original language	English (US)
Title of host publication	Proceedings of the 28th International Conference on Machine Learning, ICML 2011
Pages	377-384
Number of pages	8
State	Published - Oct 7 2011
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2021-02-09

Cite this

@inproceedings{d89ed1e86c6144d2acc79d9b2a261a36,

title = "Topic modeling with nonparametric Markov tree",

abstract = "A new hierarchical tree-based topic model is developed, based on nonparametric Bayesian techniques. The model has two unique attributes: (i) a child node in the tree may have more than one parent, with the goal of eliminating redundant sub-topics deep in the tree; and (ii) parsimonious sub-topics are manifested, by removing redundant usage of words at multiple scales. The depth and width of the tree are unbounded within the prior, with a retrospective sampler employed to adaptively infer the appropriate tree size based upon the corpus under study. Excellent quantitative results are manifested on five standard data sets, and the inferred tree structure is also found to be highly interpretable. Copyright 2011 by the author(s)/owner(s).",

author = "Haojun Chen and Dunson, {David B.} and Lawrence Carin",

note = "Generated from Scopus record by KAUST IRTS on 2021-02-09",

year = "2011",

month = oct,

day = "7",

language = "English (US)",

isbn = "9781450306195",

pages = "377--384",

booktitle = "Proceedings of the 28th International Conference on Machine Learning, ICML 2011",

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T1 - Topic modeling with nonparametric Markov tree

AU - Chen, Haojun

AU - Dunson, David B.

AU - Carin, Lawrence

N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09

PY - 2011/10/7

Y1 - 2011/10/7

N2 - A new hierarchical tree-based topic model is developed, based on nonparametric Bayesian techniques. The model has two unique attributes: (i) a child node in the tree may have more than one parent, with the goal of eliminating redundant sub-topics deep in the tree; and (ii) parsimonious sub-topics are manifested, by removing redundant usage of words at multiple scales. The depth and width of the tree are unbounded within the prior, with a retrospective sampler employed to adaptively infer the appropriate tree size based upon the corpus under study. Excellent quantitative results are manifested on five standard data sets, and the inferred tree structure is also found to be highly interpretable. Copyright 2011 by the author(s)/owner(s).

AB - A new hierarchical tree-based topic model is developed, based on nonparametric Bayesian techniques. The model has two unique attributes: (i) a child node in the tree may have more than one parent, with the goal of eliminating redundant sub-topics deep in the tree; and (ii) parsimonious sub-topics are manifested, by removing redundant usage of words at multiple scales. The depth and width of the tree are unbounded within the prior, with a retrospective sampler employed to adaptively infer the appropriate tree size based upon the corpus under study. Excellent quantitative results are manifested on five standard data sets, and the inferred tree structure is also found to be highly interpretable. Copyright 2011 by the author(s)/owner(s).

UR - http://www.scopus.com/inward/record.url?scp=80053435307&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9781450306195

SP - 377

EP - 384

BT - Proceedings of the 28th International Conference on Machine Learning, ICML 2011

ER -

Topic modeling with nonparametric Markov tree

Abstract

Bibliographical note

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