Topic distribution matrices created by topic models are typically used for document clas- sification or as features in a separate machine.
We present a statistical method for investigating group differences in the document-topic distribution vectors created by Latent Dirichlet Allocation (LDA)
Nov 29, 2023 � Conference PaperPDF Available. A Statistical Approach for Quantifying Group Difference in Topic Distributions Using Clinical Discourse Samples.
A Statistical Approach for Quantifying Group Difference in Topic Distributions Using Clinical Discourse Samples. Grace Lawley, Peter A. Heeman,�...
Apr 25, 2024 � A Statistical Approach for Quantifying Group Difference in Topic Distributions Using Clinical Discourse Samples. SIGDIAL 2023: 55-65. [+]�...
Steven Bedrick. Latest. A Statistical Approach for Quantifying Group Difference in Topic Distributions Using Clinical Discourse Samples � Computational Analysis�...
Topic distribution matrices created by topic models are typically used for document classification or as features in a separate machine learning algorithm.
We present a statistical method for investigating group differences in the document-topic distribution vectors created by Latent Dirichlet Allocation (LDA)�...
In this article we illustrate the use of a distribution-free overlapping measure as an alternative way to quantify sample differences and assess research�...
Missing: Discourse | Show results with:Discourse
This paper shows how to calculate sample standardized differences for continuous and categorical variables and how to interpret results
Missing: Quantifying Discourse