Semi-Supervised Learning over Text
Tom M. Mitchell
Machine Learning Department
Carnegie Mellon University
September 2006
Modified by Charles Ling
Statistical learning methods require LOTS of
training data
Can we use all that unlabelled text?
Outline
• Maximizing likelihood in probabilistic models
– EM for text classification
• Co-Training and redundantly predictive features
– Document classification
– Named entity recognition
– Theoretical analysis
• Sample of additional tasks
– Word sense disambiguation
– Learning HTML-based extractors
– Large-scale bootstrapping: extracting from the web
Many text learning tasks
• Document classification.
– f: Doc  Class
– Spam filtering, relevance rating, web page classification, ...
– and unsupervised document clustering
• Information extraction.
– f: Sentence  Fact, f: Doc  Facts
• Parsing
– f: Sentence  ParseTree
– Related: part-of-speech tagging, co-reference res., prep phrase attachment
• Translation
– f: EnglishDoc  FrenchDoc
1. Semi-supervised Document classification
(probabilistic model and EM)
Document Classification: Bag of Words Approach
aardvark
0
about
2
all
2
Africa
1
apple
0
anxious
0
...
gas
1
...
oil
1
…
Zaire
0
Supervised: Naïve Bayes Learner
Train:
For each class cj of documents
1. Estimate P(cj )
2. For each word wi estimate P(wi | cj )
Classify (doc):
Assign doc to most probable*class
arg max P(c j )  P( wi | c j )
j
wi doc
* assuming words are conditionally independent, given class
Accuracy vs. # training examples
For code and data, see
www.cs.cmu.edu/~tom/mlbook.html
click on “Software and Data”
What if we have labels for only some documents?
X1
Learn P(Y|X)
Y
X1
X2
X3
X4
Y
1
0
0
1
1
0
0
1
0
0
0
0
0
1
0
?
0
1
1
0
?
0
1
0
1
X2
X3
X4
EM: Repeat until convergence
1. Use probabilistic labels to train classifier h
2. Apply h to assign probabilistic labels to unlabeled data
From [Nigam et al., 2000]
E Step:
M Step:
wt is t-th word in vocabulary
Using one
labeled
example per
class
Words sorted
by P(w|course) /
P(w| : course)
20 Newsgroups
20 Newsgroups
Why/When will this work?
• What’s best case? Worst case? How can we test which
we have?
EM for Semi-Supervised Doc Classification
• If all data is labeled, corresponds to supervised training
of Naïve Bayes classifier
• If all data unlabeled, corresponds to mixture-ofmultinomial clustering
• If both labeled and unlabeled data, it helps if and only if
the mixture-of-multinomial modeling assumption is
correct
• Of course we could extend this to Bayes net models
other than Naïve Bayes (e.g., TAN tree)
• Other extensions: model negative class as mixture of N
multinomials
2. Using Redundantly Predictive Features
(Co-Training)
Redundantly Predictive Features
Professor Faloutsos
my advisor
Co-Training
Key idea: Classifier1 and ClassifierJ must:
1. Correctly classify labeled examples
2. Agree on classification of unlabeled
Answer1
Answer2
Classifier1
Classifier2
CoTraining Algorithm #1
[Blum&Mitchell, 1998]
Given: labeled data L,
unlabeled data U
Loop:
Train g1 (hyperlink classifier) using L
Train g2 (page classifier) using L
Allow g1 to label p positive, n negative examps from U
Allow g2 to label p positive, n negative examps from U
Add these self-labeled examples to L
CoTraining: Experimental Results
•
•
•
•
begin with 12 labeled web pages (academic course)
provide 1,000 additional unlabeled web pages
average error: learning from labeled data 11.1%;
average error: cotraining 5.0%
Typical run:
Co-Training for Named Entity Extraction
(i.e.,classifying which strings refer to people, places,
dates, etc.)
[Riloff&Jones 98; Collins et al., 98; Jones 05]
Answer1
Answer2
Classifier1
Classifier2
New York
I flew to ____ today
I flew to New York today.
CoTraining setting:
• wish to learn f: X  Y, given L and U drawn from P(X)
• features describing X can be partitioned (X = X1 x X2)
such that f can be computed from either X1 or X2
One result [Blum&Mitchell 1998]:
• If
– X1 and X2 are conditionally independent given Y
– f is PAC learnable from noisy labeled data
• Then
– f is PAC learnable from weak initial classifier plus unlabeled
data
Example Bootstrap learning algorithms:
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Classifying web pages [Blum&Mitchell 98; Slattery 99]
Classifying email [Kiritchenko&Matwin 01; Chan et al. 04]
Named entity extraction [Collins&Singer 99; Jones&Riloff 99]
Wrapper induction [Muslea et al., 01; Mohapatra et al. 04]
Word sense disambiguation [Yarowsky 96]
Discovering new word senses [Pantel&Lin 02]
Synonym discovery [Lin et al., 03]
Relation extraction [Brin et al.; Yangarber et al. 00]
Statistical parsing [Sarkar 01]
What to Know
• Several approaches to semi-supervised learning
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–
–
–
–
EM with probabilistic model
Co-Training
Graph similarity methods
...
See reading list below
• Redundancy is important
• Much more to be done:
–
–
–
–
–
Better theoretical models of when/how unlabeled data can help
Bootstrap learning from the web (e.g. Etzioni, 2005, 2006)
Active learning (use limited labeling time of human wisely)
Never ending bootstrap learning?
...
Further Reading
•
Semi-Supervised Learning, Olivier Chapelle, Bernhard Sch¨olkopf, and Alexander
Zien (eds.), MIT Press, 2006.
•
Semi-Supervised Learning Literature Survey, Xiaojin Zhu, 2006.
•
Unsupervised word sense disambiguation rivaling supervised methods D. Yarowsky
(1995)
"Semi-Supervised Text Classification Using EM," K. Nigam, A. McCallum, and T.
Mitchell, in Semi-Supervised Learning, Olivier Chapelle, Bernhard Sch¨olkopf, and
Alexander Zien (eds.), MIT Press, 2006.
" Text Classification from Labeled and Unlabeled Documents using EM," K. Nigam,
Andrew McCallum, Sebastian Thrun and Tom Mitchell. Machine Learning, Kluwer
Academic Press, 1999.
" Combining Labeled and Unlabeled Data with Co-Training," A. Blum and T. Mitchell,
Proceedings of the 1998 Conference on Computational Learning Theory, July 1998.
Discovering Word Senses from Text Pantel & Lin (2002)
Creating Subjective and Objective Sentence Classifiers from Unannotated Texts by
Janyce Wiebe and Ellen Riloff (2005)
Graph Based Semi-Supervised Approach for Information Extraction by Hany Hassan,
Ahmed Hassan and Sara Noeman (2006)
The use of unlabeled data to improve supervised learning for text summarization by
MR Amini, P Gallinari (2002)
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Further Reading
•
•
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•
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Yusuke Shinyama and Satoshi Sekine. Preemptive Information Extraction using
Unrestricted Relation Discovery
Alexandre Klementiev and Dan Roth. Named Entity Transliteration and Discovery
from Multilingual Comparable Corpora.
Rion L. Snow, Daniel Jurafsky, Andrew Y. Ng. Learning syntactic patterns for
automatic hypernym discovery
Sarkar. (1999). Applying Co-training methods to Statistical Parsing.
S. Brin, 1998. Extracting patterns and relations from the World Wide Web, EDBT'98
O. Etzioni et al., 2005. "Unsupervised Named-Entity Extraction from the Web: An
Experimental Study," AI Journal, 2005.