Wrapper Induction for Information Extraction

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Wrapper Induction for Information Extraction

• Nicholas Kushmerick Daniel S.Weld Robert
  Doorenbos

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Outline

• Describe wrappers.
• Formalize the wrapper construction problem as
  that of inductive generalization.
• Identify the HLRT wrapper class.
• Apply the PAC framework.
• Present a modular approach to building oracles.
• Provide empirical evaluation of our approach.

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• ExtractCCs(page P)
• Skip past first occurrence of ltBgt in P
• While next ltBgt is before next ltHRgt in P
• for each ltlk,rkgtbelongs to lt ltBgt,lt/Bgtgt,lt
  ltIgt,lt/Igtgt
• Skip past next occurrence of lk in P
• extract attribute from P to next
  occurrence of rk
• Return extracted tuples
• ExecuteHLRT(lth,t,l1,r1,..,lk,rkgt,page P)
• Skip past first occurrence of h in P
• While next l1 is before next t in P
• For each ltlk,rkgtbelongs to ltl1,r1gt,..,lt lk, rk
  gt
• Skip past next occurrence of lk in P
• Extract attribute from P to next occurrence of
  rk
• Return extracted tuples

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Constructing wrapper by induction

• Induction is the task of generalizing from
  labeled example to a hypothesis, a function for
  labeling instances.
• The wrapper construction problem is the
  following given a supply of example query
  responses, learn a wrapper for the information
  resource that generated them .

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• Wrapper Induction Details
• Instances
• Labels
• Hypotheses
• Oracles correspond to sources of example query
  responses and their labels. We split the
  traditional oracle into two parts.
• PageOracle generates example pages.
• LabelOracle produces correct labels for
  these instances.
• PAC analysis answers the question, How many
  examples must a learner see to be confident that
  its hypothesis is good enough-i.e., to be
  probably approximately correct?'

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Composing oracles

• LabelOracle is provided as input.
• Recognizers finds instances of a particular
  attribute on a page.
• These recognized instances are then corroborated
  to label the entire page.
• For example, given a recognizer for countries and
  another for country codes, corroboration produces
  an oracle that labels pages containing pairs of
  these attributes.

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Recognizer types

• Perfect accept all positive instances and reject
  all negative instances of their target attribute.
  
• Incomplete reject all negative instances but
  reject some positive instances.
• Unsound accept all positive instances but
  accept some negative instances.
• unreliable reject some positive instances and
  accept some negative instances.

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Empirical evaluation I

• 100 Internet resources
• selected randomly
• search.com
• 48 can be wrapped by HLRT.

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Empirical evaluation II.

• Another experiment measures the robustness of the
  system to the recognizers' error rates.
• The system was tested on
• (i) the OKRA email service
• (ii) the BIGBOOK telephone directory.
• OKRA has four attributes
• BIGBOOK has six.
• Runs the system with these perfect recognizers.
• two termination conditions
• (a) we ran the system until the PAC criteria was
  satisfied
• (b) we required that the learned wrapper be 100
  correct on a suite of test pages.

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• 4.9 examples are sufficient for OKRA.
• 29 for BIGBOOK.
• The number of examples required is small enough
  for practical perspective.

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• 105 examples are needed required to satisfy the
  PAC criteria.
• PAC model is too week to tightly constrain the
  induction process.

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Conclusions

• Wrapper induction works reasonably well.
• three contributions
• Formalization of the wrapper construction problem
  as induction.
• Definition of the HLRT bias, which is efficiently
  learnable in this framework.
• Using of heuristic knowledge to compose the
  algorithm's oracle.