Probabilistic Parsing II

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Probabilistic Parsing II

• (many slides adapted from slides by
• Michael Collins)

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How well do PCFGs work?

• Not very well
• a PCFG adequate to parse over 90 of the MIT
  Voyager Corpus was successful in picking the
  correct parse on only 35 of a reserved test set.
• Sample Sentences -- The MIT Voyager Corpus
• I'm currently at MIT
• What kind of food does LaGroceria serve
• Where is the closest library to MIT
• What's the closest ice cream parlor to Harvard
  University
• Is there a subway stop by the Mount Auburn
  Hospital
• Can you show me the intersection of Cambridge
  Street and Hampshire Street
• Which subway stop is closest to the library at
  forty five Pearl Street

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Voyager Experiments Results

• Results parsing reserved Voyager corpus. Ref
  Magerman Marcus 1991

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PP Attachment V-NP-PP forced choice
S
VP
PP
NP
NP
V
P

• He joined the board as a nonexecutive
  director
• Quintuple (V-attach, vjoined, n1board, pas,
  n2director)
• Training set 20,801 quintuples (V- or N-attach,
  v, n1, p, n2)
• Test set 3097 quintuples
• Development set 4059 quintuples

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Core Statistical Approach

• Estimate
• If
• Noun-attach
• Else
• Verb-attach
• Estimation using Maximum Likelihood Estimate

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The final algorithm w/ backoff
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Q How to make PCFGs sensitive to

• Lexical relationships
• Larger Contexts

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Lexical relationships paths in the parse tree
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Lexical relationships are now coded locally in
the tree itself
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The SPATTER Parser (Magerman 95)
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A Lexicalized PCFG
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Factoring rule expansion Charniak 97
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Smoothed Estimation
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Smoothed Estimation II
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Smoothed Estimation III
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Independence Assumptions
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Head Probabilities
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Rule Probabilities
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Estimating Head Probabilities
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