Machinelearning based Semistructured IE

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Machine-learning based Semi-structured IE

• Chia-Hui Chang
• Department of Computer Science Information
  Engineering
• National Central University
• chia_at_csie.ncu.edu.tw
• 9/24/2002

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Wrapper Induction

• Wrapper
• An extracting program to extract desired
  information from Web pages.
• Semi-Structure Doc. wrapper? Structure Info.
• Web wrappers wrap...
• Query-able or Search-able Web sites
• Web pages with large itemized lists
• The primary issues are
• How to build the extractor quickly?

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Semi-structured IE

• Independently of the traditional IE
• The necessity of extracting and integrating data
  from multiple Web-based sources

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Machine-Learning Based Approach

• A key component of IE systems is
• a set of extraction patterns
• that can be generated by machine learning
  algorithms.
• Extractor
• Driver Architecture
• Rule Format

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Related Work

• Shopbot
• Doorenbos, Etzioni, Weld, AA-97
• Ariadne
• Ashish, Knoblock, Coopis-97
• WIEN
• Kushmerick, Weld, Doorenbos, IJCAI-97
• SoftMealy wrapper representation
• Hsu, IJCAI-99
• STALKER
• Muslea, Minton, Knoblock, AA-99
• A hierarchical FST

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WIEN

• N. Kushmerick, D. S. Weld,
• R. Doorenbos,
• University of Washington, 1997
• http//www.cs.ucd.ie/staff/nick/

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Example 1
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Extractor for Example 1
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HLRT
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Wrapper Induction

• Induction
• The task of generalizing from labeled examples to
  a hypothesis
• Instances pages
• Labels (Congo, 242), (Egypt, 20), (Belize,
  501), (Spain, 34)
• Hypotheses
• E.g. (ltpgt, ltHRgt, ltBgt, lt/Bgt, ltIgt, lt/Igt)

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BuildHLRT
succeeds
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Other Family

• OCLR (Open-Close-Left-Right)
• Use Open and Close as delimiters for each tuple
• HOCLRT
• Combine OCLR with Head and Tail
• N-LR and N-HLRT
• Nested LR
• Nested HLRT

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Terminology

• Oracles
• Page Oracle
• Label Oracle
• PAC analysis
• is to determine how many examples are necessary
  to build an wrapper with two parameters
  accuracy ? and confidence ?
• PrE(w)lt?gt1-?, or PrE(w)gt?lt?

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Probably Approximate Correct (PAC) Analysis

• With ?0.1, ?0.1, K4, an average of 5
  tuples/page, Build HLRT must examine at least 72
  examples

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Empirical Evaluation

• Extract 48 web pages successfully.
• Weakness
• Missing attributes, attributes not in order,
  tabular data, etc.

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Softmealy

• Chun-Nan Hsu, Ming-Tzung Dung, 1998
• Arizona State University
• http//kaukoai.iis.sinica.edu.tw/chunnan/mypublic
  ations.html

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Softmealy Architecture

• Finite-State Transducers for Semi-Structured Text
  Mining
• Labeling use a interface to label example by
  manually.
• Learner FST (Finite-State Transducer)
• Extractor
• Demonstration
• http//kaukoai.iis.sinica.edu.tw/video.html

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Softmealy Wrapper

• SoftMealy wrapper representation
• Uses finite-state transducer where each distinct
  attribute permutations can be encoded as a
  successful path
• Replaces delimiters with contextual rules that
  describes the context delimiting two adjacent
  attributes

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Example
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Label the Answer Key
4???
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Finite State Transducer
????(N, M)?(N, A, M)2???
extract
extract
skip
skip
N
-U
U
skip
-N
extract
extract
skip
skip
M
-A
A
e
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Find the starting position -- Single Pass

• ?????

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Contextual based Rule Learning

• Tokens
• Separators
• SL Punc(,) Spc(1) Html(ltIgt)
• SR C1Alph(Professor) Spc(1) OAlph(of)
• Rule generalization
• Taxonomy Tree

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Tokens

• All uppercase string CALph
• An uppercase letter, followed by at least one
  lowercase letter, C1Alph
• A lowercase letter, followed by zero or more
  characters OAlph
• HTML tag HTML
• Punctuation symbol Punc
• Control characters NL(1), Tab(4), Spc(3)

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Rule Generalization
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Learning Algorithm

• Generalize each column by replacing each token
  with their least common ancestor

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Taxonomy Tree
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Generating to Extract the Body

• The contextual rules for the head and tail
  separators are
• hLC1alpha(Staff) Html(lt/H2gt) NL(1)Html(ltHRgt)
  NL(1) Html(ltULgt)
• tRHtml(lt/ULgt) NL(1) Html(ltHRgt) NL(1)
  Html(ltADDRESSgt) NL(1) Html(ltIgt) Clalpha(Please)

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More Expressive Power

• Softmealy allows
• Disjunction
• Multiple attribute orders within tuples
• Missing attributes
• Features of candidate strings

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Stalker

• I. Muslea, S. Minton, C. Knoblock,
• University of Southern California
• http//www.isi.edu/muslea/

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STALKER

• Embedded Catalog Tree
• Leaves (primitive items) ????????
• Internal nodes (items)
• Homogeneous list, or
• Heterogeneous tuple.

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EC Tree of a page
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Extracting Data from a Document

• For each node in the EC Tree, the wrapper needs a
  rule that extracts that particular node from its
  parent
• Additionally, for each list node, the wrapper
  requires a list iteration rule that decomposes
  the list into individual tuples.
• Advantages
• The hierarchical extraction based on the EC tree
  allows us to wrap information sources that have
  arbitrary many levels of embedded data.
• Second, as each node is extracted independently
  of its siblings, our approach does not rely on
  there being a fixed ordering of the items, and we
  can easily handle extraction tasks from documents
  that may have missing items or items that appear
  in various orders.

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Extraction Rules as Finite Automata

• Landmarks
• A sequence of tokens and wildcards
• Landmark automata
• A non-deterministic finite automata

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Landmark Automata (LA)

• A linear LA has one accepting state
• from each non-accepting state, there are exactly
  two possible transitions a loop to itself, and a
  transition to the next state
• each non-looping transition is labeled by a
  landmarks
• all looping transitions have the meaning consume
  all tokens until you encounter the landmark that
  leads to the next state.

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Rule Generation
Extract Credit info.
1st terminals reservation _Symbol_ _Word_
Candidate ltigt _Symbol_ _HtmlTag_
perfect Disjltigt _HtmlTag_ positive
example D3, D4 2nd uncoverD1, D2
Candidate _Symbol_
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Possible Rules
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The STALKER Algorithm
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Features

• Process is performed in a hierarchical manner.
• ??Attributes not in order????
• Use disjunctive rule ????Missing attributes????

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Multi-pass Softmealy

• Chun-Nan Hsu and Chian-Chi Chang
• Institute of Information Science
• Academia Sinica
• Taipei, Taiwan

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Multi-pass
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Tabular style document
(Quote Server)
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Tagged-list style document
(Internet Address Finder)
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Layout styles and learnability

• Tabular style
• missing attributes, ordering as hints
• Tagged-list style
• variant ordering, tags as hints
• Prediction
• single-pass for tabular style
• multi-pass for tagged-list style

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Tabular result (Quote Server)
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Tagged-list result (Internet Address Finder)
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Alternative for Tagged-List Docs
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Comparison

• Both
• can handle irregular missing attributes.
• ??????attribute,??training
• Single-pass
• ???attribute permutations ??
• Single-pass is good for tabular pages
• ???
• Multi-pass
• Attribute permutations????
• Multi-pass is good for tagged-list pages
• ???

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Experiments

• Okra(tabular pages)
• Stalker 97, 1 example tuple
• WIEN 100 , 13 example tuples, 30 trials
• SoftMealy single-pass 100, 1 example tuple, 30
  trials
• Big-book(tagged-list pages)
• Stalker 97, 8 example tuples
• WIEN perfect, 18 example tuples, 30 trials
• SoftMealy single-pass 97, 4 examples, 30 trials
• multi-pass 100, 6 examples,
  30 trials

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Experiments (Cont.)

• Quote Server
• Stalker 10 example tuples, 79, 500 trials
• WIEN the collection beyond learns capability
• SoftMealy multi-pass 85, single-pass 97
• Internet Address Finder
• Stalker 80 100, 300 trials (omitting
  multiple occurrences of Organization)
• WIEN the collection beyond learns capability
• SoftMealy multi-pass 68, single-pass 41, 99
  with the alternative approach

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References

• Kushmerick, N. Wrapper induction Efficiency and
  expressiveness. Artificial Intelligence J.
  118(1-2)15-68, 2000.
• Chun-Nan Hsu and Ming-Tzung Dung. Generating
  finite-state transducers for semistructured data
  extraction from the web. Information Systems,
  23(8)521-538, 1998.
• Chun-Nan Hsu and Chien-Chi Chang. Finite-State
  Transducers for Semi-Structured Text Mining, In
  Proceedings of IJCAI-99 Workshop on Text Mining,
  Stockholm, Sweden, 1999. Page 38-49.
• Ion Muslea, Steve Minton, Craig Knoblock.
  Hierarchical Wrapper Induction for Semistructured
  Information Sources, Journal of Autonomous Agents
  and Multi-Agent Systems, 493-114, 2001.