dan jurafsky extracng richer relaons using rules

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: f red algae, such as Gelidium, for laboratory or industrial use” •  What does Gelidium mean? •  How do you know?` Dan Jurafsky Hearst’s PaTerns for extrac'ng IS ­A rela'ons (Hearst, 1992): Automa+c Acquisi+on of Hyponyms “Y such as X ((, X)* (, and|or) X)”! “such Y as X”! “X or other Y”! “X and other Y”! “Y including X”! “Y, especially X”! Dan Jurafsky Hearst’s PaTerns for extrac'ng IS ­A rela'ons Hearst paTern Example occurrences X and other Y ...temples, treasuries, and other important civic buildings. X or other Y Bruises, wounds, broken bones or other injuries... Y such as X The bow lute, such as the Bambara ndang... Such Y as X ...such authors as Herrick, Goldsmith, and Shakespeare. Y including X ...common ­law countries, including Canada and England... Y , especially X European countries, especially France, England, and Spain... Dan Jurafsky Extrac'ng Richer Rela'ons Using Rules •  Intui+on: rela+ons oben hold between specific en++es •  located ­in (ORGANIZATION, LOCATION) •  founded (PERSON, ORGANIZATION) •  cures (DRUG, DISEASE) •  Start with Named En+ty tags to help extract rela+on! Dan Jurafsky Named En''es aren’t quite enough. Which rela'ons hold between 2 en''es? Cure? Prevent? Drug Cause? Disease Dan Jurafsky What rela'ons hold between 2 en''es? Founder? Investor? PERSON Member? Employee? President? ORGANIZATION Dan Jurafsky Extrac'ng Richer Rela'ons Using Rules and Named En''es Who holds what office in what organiza+on? PERSON, POSITION of ORG •  George Marshall, Secretary of State of the United States PERSON(named|appointed|chose|etc.) PERSON Prep? POSITION •  Truman appointed Marshall Secretary of State PERSON [be]? (named|appointed|etc.) Prep? ORG POSITION •  George Marshall was named US Secretary of State Dan Jurafsky Hand ­built paTerns for rela'ons •  Plus: •  Human patterns tend to be high-precision •  Can be tailored to specific domains •  Minus •  Human patterns are often low-recall •  A lot of work to think of all possible patterns! •  Don’t want to have to do this for every relation! •  We’d like better accuracy Relation Extraction Using paaerns to extract rela+ons Relation Extraction Supervised rela+on extrac+on Dan Jurafsky Supervised machine learning for rela'ons •  Choose a set of rela+ons we’d like to extract •...
View Full Document

Ask a homework question - tutors are online