# Topic Model Diagnostics: Assessing Domain Relevance via Topical Alignment
**Source**: https://idl.uw.edu/papers/topic-model-diagnostics
**Parent**: https://idl.uw.edu/papers
[Jason Chuang](http://jason.chuang.info), Sonal Gupta, Christopher D. Manning, [Jeffrey Heer](http://homes.cs.washington.edu/~jheer/).
Proc. International Conference on Machine Learning (ICML), 2013
[Jason Chuang](http://jason.chuang.info), Sonal Gupta, Christopher D. Manning, [Jeffrey Heer](http://homes.cs.washington.edu/~jheer/)
Proc. International Conference on Machine Learning (ICML), 2013
We present a framework to support large-scale assessment of topic models. On the top left, the correspondence chart visualizes the alignment between human-identified concepts and machine-generated latent topics. We then introduce a process to automate the calculation of topical alignment, so that analysts can compare any number of models to known domain concepts and examine the deviations.
Materials
[PDF](https://idl.cs.washington.edu/files/2013-TopicModelDiagnostics-ICML.pdf) | [Supplement](http://idl.cs.washington.edu/files/2013-TopicalModelDiagnostics-SuppMaterial.pdf)
Abstract
The use of topic models to analyze domain-specific texts often requires manual validation of the latent topics to ensure that they are meaningful. We introduce a framework to support such a large-scale assessment of topical relevance. We measure the correspondence between a set of latent topics and a set of reference concepts to quantify four types of topical misalignment: junk, fused, missing, and repeated topics. Our analysis compares 10,000 topic model variants to 200 expert-provided domain concepts, and demonstrates how our framework can inform choices of model parameters, inference algorithms, and intrinsic measures of topical quality.
BibTeX
```
@inproceedings{2013-topic-model-diagnostics,
title = {Topic Model Diagnostics: Assessing Domain Relevance via Topical Alignment},
author = {Chuang, Jason AND Gupta, Sonal AND Manning, Christopher AND Heer, Jeffrey},
booktitle = {Proc. International Conference on Machine Learning (ICML)},
year = {2013},
url = {https://idl.uw.edu/papers/topic-model-diagnostics}
}
```
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