Coupling Semi-Supervised Learning of Categories and Relations by Andrew Carlson, Justin Betteridge,...
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Transcript of Coupling Semi-Supervised Learning of Categories and Relations by Andrew Carlson, Justin Betteridge,...
Coupling Semi-Supervised Learning of Categories and
Relationsby
Andrew Carlson, Justin Betteridge, Estevam R. Hruschka Jr. and Tom M. Mitchell
School of Computer ScienceCarnegie Mellon University
presented byThomas Packer
Bootstrapped Information Extraction
• Semi-Supervised:– Seed knowledge (predicate instances & patterns)– Pattern learners (uses learned instances)– Instance learners (uses learned patterns)
• Feedback Loop:– Rel1(X, Y)
– Sent1(X, Y), Rel0(X, Y) Pat1
– Pat1: Sent2(A, B) Rel1(A, B)
Challenges and Previous Solutions
• Semantic drift: Feedback loop amplifies error and ambiguities.
• Semi-Supervised learning often suffers from being under-constrained.
• Multiple mutually-exclusive predicate learning: Positive examples of one predicate are also negative examples of others.
• Category and predicate learning: Arguments must be of certain types.
Approach
• Simultaneous bootstrapped training of multiple categories and multiple relations.
• Growing related knowledge provides constraints to guide continued learning.
• Ontology Constraints:– Mutually exclusive predicates imply negative instances
and patterns.– Hypernyms imply positive instances.– Relation argument type constraints imply positive
category and negative relation instances.
Mutual Exclusion Constraint
• “city” and “scientist” categories are mutually exclusive.
• If “Boston” is an instance of “city”, then it is also a negative instance of “scientist”.
• If “mayor of arg1” is a pattern for “city”, then it is also a negative pattern for “scientist”.
Hypernym Constraints
• “athlete” is a hyponym of “person”.• If “John McEnroe” is a positive instance of
athlete, then it is also a positive instance of “person”.
Type Checking Constraints
• The “ceoOf()” relation must have arguments of type “person” and “company”.
• If “bicycle” is not a “person” then “ceoOf(bicycle, Microsoft)” is a negative instance of “ceoOf()”.
• If “ceoOf(Steve Ballmer, Microsoft)” is true, then “Steve Ballmer” is a positive instance of “person”. “Microsoft” handled similarly.
Conclusion
• Clearly shows improvements based on constraints.
• Could probably benefit by– adding probabilistic reasoning– larger corpus– higher thresholds– more contrastive categories– other techniques discussed in this class