# Learning Interesting Categorical Attributes for Refined Data Exploration

arXiv: https://arxiv.org/abs/1711.10933 · HTML: https://mlsanity.com/paper/1711.10933/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2017.

## Built on by (most cited first, 1 of 1)

- [Automated Question Generation on Tabular Data for Conversational Data Exploration](https://mlsanity.com/paper/2407.12859/) (2024)

## Abstract (arXiv)

This work proposes and evaluates a novel approach to determine interesting categorical attributes for lists of entities. Once identified, such categories are of immense value to allow constraining (filtering) a current view of a user to subsets of entities. We show how a classifier is trained that is able to tell whether or not a categorical attribute can act as a constraint, in the sense of human-perceived interestingness. The training data is harnessed from Web tables, treating the presence or absence of a table as an indication that the attribute used as a filter constraint is reasonable or not. For learning the classification model, we review four well-known statistical measures (features) for categorical attributes---entropy, unalikeability, peculiarity, and coverage. We additionally propose three new statistical measures to capture the distribution of data, tailored to our main objective. The learned model is evaluated by relevance assessments obtained through a user study, reflecting the applicability of the approach as a whole and, further, demonstrates the superiority of the proposed diversity measures over existing statistical measures like information entropy.