---
title: "Integrating Background Knowledge Into Text Classification"
authors:
  - "Sarah Zelikovitz"
  - "Haym Hirsh"
doi: ""
url: "https://ijcai.org/proceedings/2003/232"
pdf: "https://ijcai.org/proceedings/2003/0232.pdf"
venue: "Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence"
year: 2003
pages: "1448-1448"
track: ""
keywords:
---

# Integrating Background Knowledge Into Text Classification

Sarah Zelikovitz, Haym Hirsh

*Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence*, pages 1448-1448.

## Abstract

We present a description of three different algorithms that use background knowledge to improve text classifiers. One uses the background knowledge as an index into the set of training examples. The second method uses background knowledge to reexpress the training examples. The last method treats pieces of background knowledge as unlabeled examples, and actually classifies them. The choice of background knowledge affects each method's performance and we discuss which type of background knowledge is most useful for each specific method.