analysis of bootstrapping algorithms seminar of machine learning for text mining upc, 18/11/2004
DESCRIPTION
Analysis of Bootstrapping Algorithms Seminar of Machine Learning for Text Mining UPC, 18/11/2004. Mihai Surdeanu. Goals. Introduce Steven Abney’s “Understanding the Yarowski Algorithm” (Computational Linguistics 30(3) 2004) paper - PowerPoint PPT PresentationTRANSCRIPT
Analysis of Bootstrapping Algorithms
Seminar of Machine Learning for Text
Mining UPC, 18/11/2004
Mihai Surdeanu
Goals Introduce Steven Abney’s “Understanding the
Yarowski Algorithm” (Computational Linguistics 30(3) 2004) paper
What are the bootstrapping algorithms covered and their properties?
Will skip theorem proofs What do they mean in the context of
document clustering and pattern acquisition? How do they compare with other iterative
refinement clustering algorithms and with Yangarber 2003?
Notations
WSD:x – wordj – word sensef – word/context feature
Clustering:x – documentj – category/domainf – doc feature: word, pattern
Generic Yarowski Algorithm (Y-0)
Needs a base
learner
Changes labeling only if prediction larger than
arbitrary threshold
Does not change labels of seeds
Nothing formal can be shown about Y-0.
Modified Algorithm (Y-1)
A labeled example cannot become unlabeled again.
Fixed threshol
d
Properties of Y-1 If the base learner reduces the divergence on
the labeled (or all) examples, algorithm Y-1 decreases H (cross entropy – equation (6)) at each iteration until it reaches a critical point of H
The Original Decision List Induction Algorithm (DL-0)
Smooth precision with an arbitrary
value
Pick the label given by the rule with the best
score is NOT a probability
distribution!
Nothing formal can be shown about DL-0.
The EM-based Decision List Algorithm (DL-EM)
A mixture of is used to compute (see above). Because is a probability distribution, is also a probability distribution.
Whereas in DL-0 the prediction is given by the “strongest” feature, here the algorithm permits a block of “weaker” features to outweigh the strongest feature.
DL-EM does not construct a classifier from scratch (like DL-0), but rather builds upon the previous classifier (fj
old and xold).
The EM-based Decision List Algorithm (DL-EM)
Probability that feature f was
responsible for label j for object x
Normalization over all features
Algorithm DL-EM-
A similar algorithm exists when the feature score is computed over all examples, not just the labeled ones: DL-EM-V.
What are the (0) parameters???
Properties of DL-EM-*
Y-1/DL-EM- and Y-1/DL-EM-V decrease H at each iteration until they reach a critical point of H (local minimum).
Algorithm DL-1-R
“Raw” precision
Mixture of feature scores
Algorithm DL-1-VS
Precision with variable smoothing for each
feature
Mixture of feature scores
Properties of DL-1-*
Y-1/DL-1-R minimizes K (an upper limit on H) over labeled examples
Y-1/DL-1-VS minimizes K over all examples X
So far…
Y-0/DL-0 – original Yarowski algorithm. Can not be shown to minimize H or K.
Y-1/DL-EM- and Y-1/DL-EM-V minimize H
Y-1/DL-1-R and Y-1/DL-1-VS minimize K
Sequential Algorithms All previous algorithms do “parallel”
updates, in the sense that the parameters {fj} are all recomputed at every iteration.
Sequential algorithms: one feature is selected at each iteration: St+1 = St U {ft}
Only the score of the selected feature and the scores of the documents containing a chosen feature are recomputed.
More flexible – shown to converge for more base learners.
Algorithm YS
Choose a feature that:(1) Is not seed(2) Is seen in training(3) Its score changed
Base Learners for YS
Biased towards the feature that maximizes raw precision = anti-smoothing
Properties of YS-*
YS-P and YS-R reduce K in every iteration.
YS-FS reduce K in every iteration for new features.
Yarowski versus Co-training Co-training attempts to maximize
agreement on unlabeled data between classifiers trained on different “views” of the data.
The modified Yarowski algorithms introduced in this paper reduce the upper limit on entropy (H), similarly to co-training.
Co-training uses an assumption of at least two independent views of the data. Hence it is more restricted.
YS versus Yangarber (1)
NOT a probability distribution
set = 1, else = 0
Recompute
YS versus Yangarber (2) Yangarber does not require the
computation of Y, as its goal is to learn patterns (features) relevant for each label (category) A plus for Yangarber as Yx = ŷ is a VERY
strong statement in document classification = classifies a document based on the limited information available in this iteration
Y can be computed as a side effect when the algorithm completes. This is used as an indirect evaluation.
YS versus Yangarber (3)
The base learner for Yangarber generates scores that are NOT probability distributions! Hard to analyze the algorithm formally!
fj = raw_precision(f,j) * log(how many documents contain f)
???This part similar to YS-R
Bootstrapping versus K-Means and EM
K-Means and Bootstrapping “hard” classify objects in each iteration: Yx = ŷ. EM (and Yangarber) compute Y only in the last iteration.
I think K-Means and EM converge more rapidly because they accumulate more features faster than bootstrapping.
In K-Means basically after the first iteration all features are in use.
In FS (and Yangarber) only one (or a very small number) of the features is selected in every iteration.
Conclusions Abney simple modifications of the
Yarowski bootstrapping algorithm can be formally shown to converge to a local minimum (like EM)
Based on this work Yangarber (and Riloff) are far from the formalization required to show that they converge
Is there a better algorithm for pattern learning?