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Coupling Semi-Supervised Learning of Categories and Relations. Andrew Carlson, Justin Betteridge , Estevam R. Hruschka Jr., and Tom M. Mitchell Carnegie Mellon University. The Problem.

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coupling semi supervised learning of categories and relations

Coupling Semi-Supervised Learning of Categories and Relations

Andrew Carlson, Justin Betteridge, Estevam R. Hruschka Jr., and Tom M. Mitchell

Carnegie Mellon University

CS 652, Peter Lindes

slide2

The Problem

“We present an approach to semi-supervised learning that yields more accurate results by coupling the training of many information extractors.”

CS 652, Peter Lindes

slide4

Predefined Categories

    • Unary predicates (instances are noun phrases)
    • Mutually exclusive relationships
    • Some subset relationships
    • Flag: proper nouns, common nouns, or both
    • 10-20 seed instances
    • 5 seed patterns (automatically derived - Hearst, 1992)
  • Predefined Relations
    • Binary predicates (an instance is a pair of noun phrases)
    • Mutually exclusive relationships
    • 10-20 seed instances
    • No seed patterns

CS 652, Peter Lindes

slide5

The Predicates

CS 652, Peter Lindes

slide6

Taken from “a 200-million page web crawl”

  • Filtered for English “using a stop word ratio threshold”
  • Filtered out web spam and adult content “using a ‘bad word’ list”
  • Segmented, tokenized, and tagged
  • Noisy sentences filtered out
  • 514-million sentences used for experiment

CS 652, Peter Lindes

evaluation
Evaluation
  • 3 Questions:
    • “Can CBL iterate many times and still achieve high precision?”
    • “How helpful are the types of coupling that we employ?”
    • “Can we extend existing semantic resources?”
  • 3 Configurations
    • Full
    • NS: no sharing of promoted items, seeds shared
    • NCR: no type checking

CS 652, Peter Lindes

results precision
Results - Precision

Categories

Relations

Precision estimated by human judging of correctness for 30 samples of each predicate.

CS 652, Peter Lindes

results recall
Results - Recall

Promoted categories and relations – 15 iterations

“At this stage of development, obtaining high recall is not a priority … it is our hope that high recall will come with time.”

CS 652, Peter Lindes

example extracted facts
Example Extracted Facts

“We have presented a method of coupling the semi-supervised learning of categories and relations and demonstrated empirically that the coupling forestalls the problem of semantic drift associated with bootstrap learning methods.”

CS 652, Peter Lindes

comparison to freebase
Comparison to Freebase

“… our methods can contribute new facts to existing resources.”

CS 652, Peter Lindes