Unsupervised named-entity extraction from the Web: An experimental study

@article{Etzioni2005UnsupervisedNE,
  title={Unsupervised named-entity extraction from the Web: An experimental study},
  author={Oren Etzioni and Michael J. Cafarella and Doug Downey and Ana-Maria Popescu and Tal Shaked and Stephen Soderland and Daniel S. Weld and Alexander Yates},
  journal={Artif. Intell.},
  year={2005},
  volume={165},
  pages={91-134}
}
The KNOWITALL system aims to automate the tedious process of extracting large collections of facts ( e.g., names of scientists or politicians) from the Web in an unsupervised, domain-independent, and scalable manner. The paper presents an overview of K NOWITALL ’s novel architecture and design principles, emphasizing its distinctive ability to extract information without any hand-labeled training examples. In its first major run, K NOWITALL extracted over 50,000 class instances, but suggested a… CONTINUE READING
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  • In concert, our methods gave K NOWITALL a 4-fold to 8-fold increase in recall at precision of 0.90, and discovered over 10,000 cities missing from the Tipster Gazetteer.

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