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


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 KNOWITALL’s novel architecture and design principles, emphasizing its distinctive ability to extract information without any hand-labeled training examples. In its first major run, KNOWITALL extracted over 50,000 facts, but suggested a challenge: How can we improve KNOWITALL’s recall and extraction rate without sacrificing precision? This paper presents three distinct ways to address this challenge and evaluates their performance. Pattern Learning learns domain-specific extraction rules, which enable additional extractions. Subclass Extraction automatically identifies sub-classes in order to boost recall. List Extraction locates lists of class instances, learns a “wrapper” for each list, and extracts elements of each list. Since each method bootstraps from KNOWITALL’s domainindependent methods, the methods also obviate hand-labeled training examples. The paper reports on experiments, focused on named-entity extraction, that measure the relative efficacy of each method and demonstrate their synergy. In concert, our methods gave KNOWITALL a 4-fold to 8-fold increase in recall, while maintaining high precision, and discovered over 10,000 cities missing from the Tipster Gazetteer.

DOI: 10.1016/j.artint.2005.03.001

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@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} }