K-Means Subject Matter Expert Refined Topic Model Methodology

K-Means Subject Matter Expert Refined Topic Model Methodology

We propose an innovative technique using K-means clustering to estimate the posterior topic distributions in Latent Dirichlet topic models as an alternative to the collapsed Gibbs sampling technique. This research also develops a topic modeling software instantiation of the K-means Subject Matter Ex...

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Hauptverfasser: Parker,Nathan, Allen,Theodore T, Sui,Zhenhuan
Format: Report
Sprache:eng
Schlagworte:
clustering
K-means
KSMERT
Latent Dirichlet Allocation
LDA
SMERT
Subject Matter Expert Refined Topic
Text Analysis
Topic Models
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creator Parker,Nathan
Allen,Theodore T
Sui,Zhenhuan
description We propose an innovative technique using K-means clustering to estimate the posterior topic distributions in Latent Dirichlet topic models as an alternative to the collapsed Gibbs sampling technique. This research also develops a topic modeling software instantiation of the K-means Subject Matter Expert Refined Topic methodology using the Visual Basic for Applications programming language. This topic modeling software is deployable across the majority of the Department of Defense computing environments and allows analysts to develop topic models using a graphical user interface.
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source DTIC Technical Reports
subjects clustering
K-means
KSMERT
Latent Dirichlet Allocation
LDA
SMERT
Subject Matter Expert Refined Topic
Text Analysis
Topic Models
title K-Means Subject Matter Expert Refined Topic Model Methodology
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