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

Oren Etzioni*, Michael Cafarella, Doug Downey, Ana Maria Popescu, Tal Shaked, Stephen Soderland, Daniel S. Weld, Alexander Yates

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

680 Scopus citations

Abstract

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 class instances, 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 (e.g., "chemist" and "biologist" are identified as sub-classes of "scientist"). 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 domain-independent methods, the methods also obviate hand-labeled training examples. The paper reports on experiments, focused on building lists of named entities, 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 at precision of 0.90, and discovered over 10,000 cities missing from the Tipster Gazetteer.

Original languageEnglish (US)
Pages (from-to)91-134
Number of pages44
JournalArtificial Intelligence
Volume165
Issue number1
DOIs
StatePublished - Jun 2005

Keywords

  • Information Extraction
  • Pointwise mutual information
  • Question answering
  • Unsupervised

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence

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