---
title: "Concept Labeling: Building Text Classifiers with Minimal Supervision"
authors:
  - "Vijil Chenthamarakshan"
  - "Prem Melville"
  - "Vikas Sindhwani"
  - "Richard D. Lawrence"
doi: "10.5591/978-1-57735-516-8/IJCAI11-208"
url: "https://ijcai.org/proceedings/2011/208"
pdf: "https://ijcai.org/proceedings/2011/0208.pdf"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "1225-1229"
track: ""
keywords:
---

# Concept Labeling: Building Text Classifiers with Minimal Supervision

Vijil Chenthamarakshan, Prem Melville, Vikas Sindhwani, Richard D. Lawrence

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 1225-1229. DOI: [10.5591/978-1-57735-516-8/IJCAI11-208](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-208)

## Abstract

The rapid construction of supervised text classification models is becoming a pervasive need across many modern applications. To reduce human-labeling bottlenecks, many new statistical paradigms (e.g., active, semi-supervised, transfer and multi-task learning) have been vigorously pursued in recent literature with varying degrees of empirical success. Concurrently, the emergence of Web 2.0 platforms in the last decade has enabled a world-wide, collaborative human effort to construct a massive ontology of concepts with very rich, detailed and accurate descriptions. In this paper we propose a new framework to extract supervisory information from such ontologies and complement it with a shift in human effort from direct labeling of examples in the domain of interest to the much more efficient identification of concept-class associations. Through empirical studies on text categorization problems using the Wikipedia ontology, we show that this shift allows very high-quality models to be immediately induced at virtually no cost.