---
title: "A Machine Learning Approach to Building Domain-Specific Search Engines"
authors:
  - "Andrew McCallum"
  - "Kamal Nigam"
  - "Jason Rennie"
  - "Kristie Seymore"
doi: ""
url: "https://ijcai.org/proceedings/1999/94"
pdf: "https://ijcai.org/proceedings/1999/0094.pdf"
venue: "Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence"
year: 1999
pages: "662-666"
track: "Machine Learning"
keywords:
---

# A Machine Learning Approach to Building Domain-Specific Search Engines

Andrew McCallum, Kamal Nigam, Jason Rennie, Kristie Seymore

*Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence*, pages 662-666.

## Abstract

Domain-specific search engines are becoming increasingly popular because they offer increased accuracy and extra features not possible with general, Web-wide search engines. Unfortunately, they are also difficult and timeconsuming to maintain. This paper proposes the use of machine learning techniques to greatly automate the creation and maintenance of domain-specific search engines. We describe new research in reinforcement learning, text classification and information extraction that enables efficient spidering, populates topic hierarchies, and identifies informative text segments. Using these techniques, we have built a demonstration system: a search engine for computer science research papers available at www. cora.justresearch. com.