Topic Model Diagnostics: Assessing Domain Relevance via Topical Alignment
| 
139 | 
63 | 
0
Abstract
The use of topic models to analyze domainspecific 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 expertprovided domain concepts, and demonstrates how our framework can inform choices of model parameters, inference algorithms, and intrinsic measures of topical quality.
还没有评论，说两句吧！
热门资源
A Mathematical Mo...
Direct democracy, where each voter casts one vo...
Learning to Predi...
Much of model-based reinforcement learning invo...
Hierarchical Task...
We extend hierarchical task network planning wi...
The Variational S...
Unlike traditional images which do not offer in...
Shape-based Autom...
We present an algorithm for automatic detection...
智能在线
400-630-6780
聆听.建议反馈
E-mail: support@tusaishared.com
资源分类
