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

  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.},
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 facts, but suggested a challenge… CONTINUE READING
Highly Influential
This paper has highly influenced 83 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 1,595 citations. REVIEW CITATIONS
This paper has been referenced on Twitter 1 time. VIEW TWEETS

From This Paper

Figures, tables, results, and topics from this paper.

Key Quantitative Results

  • 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.


Publications citing this paper.
Showing 1-10 of 746 citations

1,595 Citations

Citations per Year
Semantic Scholar estimates that this publication has 1,595 citations based on the available data.

See our FAQ for additional information.