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Pachinko allocation: DAG-structured ...

Li, Wei.

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Pachinko allocation: DAG-structured mixture models of topic correlations.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Pachinko allocation: DAG-structured mixture models of topic correlations./
Author:	Li, Wei.
Description:	100 p.
Notes:	Adviser: Andrew McCallum.
Contained By:	Dissertation Abstracts International68-11B.
Subject:	Artificial Intelligence. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3289214
ISBN:	9780549330233

Pachinko allocation: DAG-structured mixture models of topic correlations.
Li, Wei.

Pachinko allocation: DAG-structured mixture models of topic correlations. - 100 p.

Adviser: Andrew McCallum.

Thesis (Ph.D.)--University of Massachusetts Amherst, 2007.

Statistical topic models are increasingly popular tools for summarization and manifold discovery in discrete data. However, the majority of existing approaches capture no or limited correlations between topics. We propose the pachinko allocation model (PAM), which captures arbitrary, nested, and possibly sparse correlations between topics using a directed acyclic graph (DAG). We present various structures within this framework, different parameterizations of topic distributions, and an extension to capture dynamic patterns of topic correlations. We also introduce a non-parametric Bayesian prior to automatically learn the topic structure from data. The model is evaluated on document classification, likelihood of held-out data, the ability to support fine-grained topics, and topical keyword coherence. With a highly-scalable approximation, PAM has also been applied to discover topic hierarchies in very large datasets.

ISBN: 9780549330233Subjects--Topical Terms:

769149
Artificial Intelligence.

Pachinko allocation: DAG-structured mixture models of topic correlations.
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020 $a 9780549330233
035 $a (UMI)AAI3289214
035 $a AAI3289214
040 $a UMI $c UMI
100 1 $a Li, Wei. $3 884990
245 1 0 $a Pachinko allocation: DAG-structured mixture models of topic correlations.
300 $a 100 p.
500 $a Adviser: Andrew McCallum.
500 $a Source: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7440.
502 $a Thesis (Ph.D.)--University of Massachusetts Amherst, 2007.
520 $a Statistical topic models are increasingly popular tools for summarization and manifold discovery in discrete data. However, the majority of existing approaches capture no or limited correlations between topics. We propose the pachinko allocation model (PAM), which captures arbitrary, nested, and possibly sparse correlations between topics using a directed acyclic graph (DAG). We present various structures within this framework, different parameterizations of topic distributions, and an extension to capture dynamic patterns of topic correlations. We also introduce a non-parametric Bayesian prior to automatically learn the topic structure from data. The model is evaluated on document classification, likelihood of held-out data, the ability to support fine-grained topics, and topical keyword coherence. With a highly-scalable approximation, PAM has also been applied to discover topic hierarchies in very large datasets.
590 $a School code: 0118.
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Computer Science. $3 626642
650 4 $a Statistics. $3 517247
690 $a 0463
690 $a 0800
690 $a 0984
710 2 $a University of Massachusetts Amherst. $b Computer Science. $3 1023848
773 0 $t Dissertation Abstracts International $g 68-11B.
790 $a 0118
790 1 0 $a Blei, David $e committee member
790 1 0 $a Croft, W. Bruce $e committee member
790 1 0 $a Mahadevan, Sridhar $e committee member
790 1 0 $a McCallum, Andrew, $e advisor
790 1 0 $a Staudenmayer, John $e committee member
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3289214