Transcript Who needs this Simpsons book?

Confidence-Weighted
Linear Classification
Mark Dredze, Koby Crammer
University of Pennsylvania
Fernando Pereira
Penn  Google
• Big datasets, large
number of features
• Many features are
only weakly
correlated with
target label
• Heavy-tailed feature
distribution
• Linear classifiers:
features are
associated with
word counts
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Count
Natural Language Processing
Feature Rank
Sentiment Classification
• Who needs this Simpsons book?
You DOOOOOOOO
This is one of the most extraordinary
volumes I've ever encountered
encapsulating a television series … .
Exhaustive, informative, and ridiculously
entertaining, it is the best accompaniment
to the best television show … . Even if you
only "enjoy" the Simpsons (as opposed to
being a raving fanatic, which most people
who watch the show are, after all … Very
highly recommended!
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Sentiment Classification
• Many positive reviews with the word best
Wbest
• Later negative review
– “boring book – best if you want to sleep in seconds”
• Linear update will reduce both
Wbest Wboring
• But best appeared more than boring
• How to adjust weights at different rates?
Wboring Wbest
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Linear Classifiers
Instance to
be
classified
Weight
vector
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Online Learning
• Memory-efficient, simple to implement, often
competitive
• Successive rounds
–
–
–
–
–
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Get an input instance
Output a prediction
Receive a feedback label
Compute loss
Update the prediction rule
Span-based Update Rules
• The weight vector is a linear combination of
examples
• Two rate schedules (among others):
Weight of
Learning rate
Learning rate
– Perceptron
algorithm,
feature
f
– Passive-aggressive
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Target label, -1
conservative:
or 1
Value of feature f
of instance
Distributions in Version Space
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Mean weight-vector
Qu
ickTime™anda
com
ar enee
dde
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t oprs
ees
esor
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ict ure.
Example
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Margin as a Random Variable
• Signed margin
is a Gaussian-distributed variable
• Thus:
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Weight Vector (Version) Space
Place most of the
probability mass in this
region
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Passive Step
Nothing to do, most
weight vectors already
classify the example
correctly
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Aggressive Step
Mean moved past
the mistake line
(large margin)
The covariance is
Project the current
shirked in the
Gaussian distribution
direction of the
onto the half-space
new example
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PA-like Update
• PA:
Confidence
Parameter
• New Update :
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The Optimization Problem
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decompressor
are needed to see this picture.
convex
such that
where
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QuickTime™ and a
decompressor
are needed to see this picture.
QuickTime™and
andaa
QuickTime™
QuickTime™
and a
decompressor
decompressor
decompressor
areneeded
neededto
tosee
seethis
thispicture.
picture.
are
are needed
to see
this picture.
not convex!
Simplified Optimization Problem
QuickTime™ and a
decompressor
are needed to see this picture.
convex!
• Exact variance method
• High dimension  restrict to diagonal
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Approximate Diagonal Algorithm
• Approximate by projecting onto the diagonal
• Closed form solution
QuickTime™ and a
decompressor
are needed to see this picture.
variable learning rate
scaled counts (binary features)
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Visualizing Learning Rates
30 rounds
Full
• 20 features, only two informative
• Covariance ellipses (20 x cov)
– Black: the two informative features
– Blue: several pairs of noise features
– Green: target weight vector
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Diagonal
Visualizing Learning Rates
90 rounds
Full (2 x)
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Diagonal (20 x)
Experiments
• Online to batch :
– Multiple passes over the training data
– Evaluate on a different test set after each
pass
– Compute error/accuracy
• Binary problems from
– Newsgroups
– Reuters
• Sentiment classification
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Data
• Sentiment
– Sentiment reviews from 6 Amazon domains (Blitzer et al)
– Classify a product review as either positive or negative
• Reuters, pairs of labels
– Three divisions:
• Insurance: Life vs. Non-Life, Business Services: Banking vs. Financial, Retail
Distribution: Specialist Stores vs. Mixed Retail.
– Bag of words representation with binary features.
• 20 newsgroups, pairs of labels
– Three divisions:
• comp.sys.ibm.pc.hardware vs. comp.sys.mac.hardware.instances,
sci.electronics vs. sci.med.instances, and talk.politics.guns vs.
talk.politics.mideast.instances.
– Bag of words representation with binary features.
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Typical Performance (20 NG)
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Summary Statistics
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Parallel Training
• Split large data into disjoint sets
• Train using each set independently
• Combine resulting classifiers
– Average Average performance of individual classifiers
– Uniform mean of weight vectors
– Weighted mean of weight vectors using confidence
information
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Parallel Training
•
•
•
•
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Sentiment ~1M ; Reuters ~0.8M
#Features/#Docs: Sentiment ~13 ; Reuters ~0.35
Performance degrades with number of splits
Weighting improves performance
Summary
• Online learning is fast and effective …
… but NLP data has skewed feature distributions
• Confidence-weighted linear prediction represents
explicitly how much to trust feature weights
– Higher accuracy than previous methods
– Faster convergence
– Effective model combination
• Current work:
– Direct convex version of original optimization
– Batch algorithm
– Explore full covatiance versions
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