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SLIDE 6

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Unannotated Texts Ex: anthrax, ebola, cholera, flu, plague Ex: was infected with <np>

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Ex: smallpox, tularemia, botulism noun Co-Occurrence Statistics Nouns

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(LDP(“)'C"”("&<(“>#,/C#0(/1',8”(,-*(“&)#,(”? Sense Training Example A !zonal distribution of plant life from the! A !many dangers to plant and animal life! ? !union responses to plant closures! ? !company said the plant still operating! B !copper manufacturing plant found that! B !keep a manufacturing plant profitable!

Also exploited “one sense per discourse” heuristic.

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Full-string=New_York Location Full-string=California Location Full-string=U.S. Location Contains(Mr.) Person Contains(Incorporated) Organization Full-string=Microsoft Organization Full-string=I.B.M. Organization

rule SPELLING RULES rule CONTEXTUAL RULES Unannotated

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SLIDE 8

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Poor seeding can lead to a variety of problems:

  • Bootstrapping learns low-frequency cases

high precision but low recall

  • Bootstrapping thrashes
  • nly subsets learned
  • Bootstrapping goes astray / gets derailed

the wrong concept learned

  • Bootstrapping sputters and dies

nothing learned

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Want as much coverage and contextual diversity as possible! Bad animal seeds: coatimundi, giraffe, terrier

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Seeding instances should be UNAMBIGUOUS Bad animal seeds: bat, jaguar, turkey Ambiguous seeds create noisy training data.

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SLIDE 9

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