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dan jurafsky extracng richer relaons using rules

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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 •...
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