A Machine Learning Approach to Building Domain-Specific Search Engines
Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence. Machine Learning. 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.
@inproceedings{ijcai1999p94,
title = {A Machine Learning Approach to Building Domain-Specific Search Engines},
author = {McCallum, Andrew and Nigam, Kamal and Rennie, Jason and Seymore, Kristie},
booktitle = {Proceedings of the Sixteenth International Joint Conference on
Artificial Intelligence, {IJCAI-99}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Thomas Dean},
pages = {662--666},
year = {1999},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1999/94},
}
TY - CPAPER TI - A Machine Learning Approach to Building Domain-Specific Search Engines AU - McCallum, Andrew AU - Nigam, Kamal AU - Rennie, Jason AU - Seymore, Kristie T2 - Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI-99 PB - International Joint Conferences on Artificial Intelligence PY - 1999 SP - 662 EP - 666 UR - https://ijcai.org/proceedings/1999/94 ER -Copied
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