Name
Abstract | ||
|---|---|---|
Many predictive tasks, such as diagnosing a patient based on their medical chart, are ultimately defined by the decisions of human experts. Unfortunately, encoding expertsu0027 knowledge is often time consuming and expensive. We propose a simple way to use fuzzy and informal knowledge from experts to guide discovery of interpretable latent topics in text. The underlying intuition of our approach is that latent factors should be informative about both correlations in the data and a set of relevance variables specified by an expert. Mathematically, this approach is a combination of the information bottleneck and Total Correlation Explanation (CorEx). We give a preliminary evaluation of Anchored CorEx, showing that it produces more coherent and interpretable topics on two distinct corpora. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Machine Learning | Computer science,Fuzzy logic,Corex,Intuition,Correlation,Artificial intelligence,Total correlation,Information bottleneck method,Machine learning,Encoding (memory) |
DocType | Volume | Citations |
Journal | abs/1606.07043 | 2 |
PageRank | References | Authors |
0.37 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kyle Reing | 1 | 2 | 0.71 |
| David Kale | 2 | 220 | 13.58 |
| Greg Ver Steeg | 3 | 243 | 32.99 |
| Aram Galstyan | 4 | 1033 | 94.05 |