Discriminating Word Senses Using McQuittys Similarity Analysis

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Discriminating Word Senses Using McQuittys
Similarity Analysis

• Amruta Purandare
• University of Minnesota, Duluth
• Advisor Dr Ted Pedersen
• Research supported by National Science Foundation
  (NSF)
• Faculty Early Career Development Award (0092784)

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Discriminating line
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Introduction

• What is Word Sense Discrimination ?
• Unsupervised learning

Clusters
Training
Features
Test
Feature Vectors
similarity matrix
evaluate
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Representing context

• Features (from training)
• Bi grams
• Unigrams
• Second Order Co-occurrences/SOCs (Schütze98)
• Mixture
• Feature vectors (Binary)
• Measuring similarity
• Cosine
• Match

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Feature examples
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McQuittys method

• Pedersen Bruce, 1997
• Agglomerative
• UPGMA / Average Link
• Stopping rules
• Number of clusters
• Score cutoff

xy/2
y
x
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Evaluation
sense1
( Maj )
sense2
sense3
sense4
c2
c3
c1
c4
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Evaluation
Accuracy38/550.69
sense3
sense4
sense1
sense2
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Majority Sense Classifier
Maj. 17/550.31
sense2
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Experimental Data
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Scope of the experiments

• 584 experiments (73 4 2)
• 73 Words 72 Senseval-2, LINE
• 4 Features Bi grams, Unigrams, SOCs, Mix
• 2 Similarity Measures Match, Cosine
• Window 5
• for Bi grams and SOCs
• Frequency cutoff 2

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Senseval-2 Results POS wise
29 NOUNS
28verbs
15 adjs
Maj0.57
Maj0.51
Maj0.64
No of words of a POS for which experiment
obtained accuracy more than Majority
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Senseval-2 Results Feature wise
SOC
UNI
BI
32
18
38
72 words X 2 measures 144
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Senseval-2 Results Measure wise
COS
MAT
49
39
72 words x 3 features 216
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Line Results
Maj 0.16
On uniform distribution of 6 senses
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Sample Confusion Table (fine.soc.cos)
S0 elegant S1 small grained S2 superior S3
satisfactory S4 thin
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precision 36/60 60.00
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Conclusions

• Small set of SOCs was powerful
• Half the number of unigrams/bigrams
• Scaling done by Cosine helps !
• Need more training data!
• Need to improve feature
• Selection (Tests of associations)
• extraction (Stemming)
• matching (Fuzzy matching)
• strategies for bi grams
• Explore new features
• POS
• Collocations

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Recent work

• PDL implementation
• Cluto - Clustering Toolkit
• http//www-users.cs.umn.edu/karypis/cluto
• 6 clustering methods, 12 merging criteria
• Plans
• Comparing clustering in
• similarity space Vs vector space (Schütze, 1998)
• Stopping rules

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Sense labeling
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Software Packages

• SenseClusters (Our Discrimination Toolkit)
• http//www.d.umn.edu/tpederse/senseclusters.html
  
• PDL (Used to implement clustering algorithms)
• http//pdl.perl.org/
• NSP (Used for extracting features)
• http//www.d.umn.edu/tpederse/nsp.html
• SenseTools (Used for preprocessing, feature
  matching)
• http//www.d.umn.edu/tpederse/sensetools.html
• Cluto (Clustering Toolkit)
• http//www-users.cs.umn.edu/karypis/cluto