Ijcai 2007 Pedersen

January 6, 2007 IJCAI-2007 Tutorial 1

Language Independent Methods of Clustering Similar Contexts

(with applications)

Ted PedersenUniversity of Minnesota, Duluth

[email protected]

http://www.d.umn.edu/~tpederse/SCTutorial.html


Language Independent Methods

• Do not utilize syntactic information– No parsers, part of speech taggers, etc. required

• Do not utilize dictionaries or other manually created lexical resources

• Based on lexical features selected from corpora – Assumption: word segmentation can be done by

looking for white spaces between strings• No manually annotated data, methods are

completely unsupervised in the strictest sense


A Note on Tokenization

• Default tokenization is white space separated strings

• Can be redefined using regular expressions– e.g., character n-grams (4 grams)– any other valid regular expression


Clustering Similar Contexts

• A context is a short unit of text– often a phrase to a paragraph in length,

although it can be longer

• Input: N contexts

• Output: K clusters– Where each member of a cluster is a context

that is more similar to each other than to the contexts found in other clusters


Applications

• Headed contexts (focus on target word)– Name Discrimination– Word Sense Discrimination

• Headless contexts – Email Organization– Document Clustering– Paraphrase identification

• Clustering Sets of Related Words


Tutorial Outline• Identifying Lexical Features• First Order Context Representation

– native SC : context as vector of features

• Second Order Context Representation– LSA : context as average of vectors of contexts– native SC : context as average of vectors of features

• Dimensionality reduction• Clustering• Hands-On Experience


SenseClusters

• A free package for clustering contexts– http://senseclusters.sourceforge.net– SenseClusters Live! (Knoppix CD)

• Perl components that integrate other tools– Ngram Statistics Package– CLUTO– SVDPACKC– PDL

http://senseclusters.sourceforge.net/


Many thanks…• Amruta Purandare (M.S., 2004)

– Now PhD student in Intelligent Systems at the University of Pittsburgh

– http://www.cs.pitt.edu/~amruta/

• Anagha Kulkarni (M.S., 2006)– Now PhD student at the Language Technologies

Institute at Carnegie-Mellon University– http://www.cs.cmu.edu/~anaghak/

• Ted, Amruta, and Anagha were supported by the National Science Foundation (USA) via CAREER award #0092784

http://www.cs.pitt.edu/~amruta/



http://www.cs.cmu.edu/~anaghak


Background and Motivations


Headed and Headless Contexts• A headed context includes a target word

– Our goal is to cluster the target word based on the surrounding contexts

– The focus is on the target word and making distinctions among word meanings

• A headless context has no target word– Our goal is to cluster the contexts based on

their similarity to each other– The focus is on the context as a whole and

making topic level distinctions


Headed Contexts (input)

• I can hear the ocean in that shell.

• My operating system shell is bash.

• The shells on the shore are lovely.

• The shell command line is flexible.

• An oyster shell is very hard and black.


Headed Contexts (output)

• Cluster 1: – My operating system shell is bash.– The shell command line is flexible.

• Cluster 2:– The shells on the shore are lovely.– An oyster shell is very hard and black.– I can hear the ocean in that shell.


Headless Contexts (input)

• The new version of Linux is more stable and better support for cameras.

• My Chevy Malibu has had some front end troubles.

• Osborne made one of the first personal computers.

• The brakes went out, and the car flew into the house.

• With the price of gasoline, I think I’ll be taking the bus more often!


Headless Contexts (output)

• Cluster 1:– The new version of Linux is more stable and better

support for cameras.– Osborne made one of the first personal computers.

• Cluster 2: – My Chevy Malibu has had some front-end troubles.– The brakes went out, and the car flew into the house. – With the price of gasoline, I think I’ll be taking the bus

more often!


Web Search as Application

• Snippets returned via Web search are headed contexts since they include the search term– Name Ambiguity is a problem with Web search.

Results mix different entities– Group results into clusters where each cluster is

associated with a unique underlying entity

• Pages found by following search results can also be treated as headless contexts


Name Discrimination


George Millers!


Email Foldering as Application

• Email (public or private) is made up of headless contexts– Short, usually focused…

• Cluster similar email messages together – Automatic email foldering– Take all messages from sent-mail file or inbox

and organize into categories


Clustering News as Application

• News articles are headless contexts– Entire article or first paragraph– Short, usually focused

• Cluster similar articles together, can also be applied to blog entries and other shorter units of text


What is it to be “similar”?

• You shall know a word by the company it keeps– Firth, 1957 (Studies in Linguistic Analysis)

• Meanings of words are (largely) determined by their distributional patterns (Distributional Hypothesis)– Harris, 1968 (Mathematical Structures of Language)

• Words that occur in similar contexts will have similar meanings (Strong Contextual Hypothesis)– Miller and Charles, 1991 (Language and Cognitive Processes)

• Various extensions…– Similar contexts will have similar meanings, etc.– Names that occur in similar contexts will refer to the same

underlying person, etc.


General Methodology

• Represent contexts to be clustered using first or second order feature vectors– Lexical features

• Reduce dimensionality to make vectors more tractable and/or understandable (optional)– Singular value decomposition

• Cluster the context vectors– Find the number of clusters– Label the clusters

• Evaluate and/or use the contexts!


Identifying Lexical Features

Measures of Association and

Tests of Significance


What are features?

• Features are the salient characteristics of the contexts to be clustered

• Each context is represented as a vector, where the dimensions are associated with features

• Contexts that include many of the same features will be similar to each other


Feature Selection Data

• The contexts to cluster (evaluation/test data)– We may need to cluster all available data, and not

hold out any for a separate feature identification step

• A separate larger corpus (training data), esp. if we cluster a very small number of contexts– local training – corpus made up of headed contexts– global training – corpus made up of headless contexts

• Feature selection data may be either the evaluation/test data, or a separate held-out set of training data


Feature Selection Data

• Test / Evaluation data : contexts to be clustered– Assume that the feature selection data is the test

data, unless otherwise indicated

• Training data – a separate corpus of held out feature selection data (that will not be clustered)– may need to use if you have a small number of

contexts to cluster (e.g., web search results)– This sense of “training” due to Schütze (1998)

• does not mean labeled• simply an extra quantity of text


Lexical Features

• Unigram– a single word that occurs more than X times in feature

selection data and is not in stop list

• Stop list– words that will not be used in features– usually non-content words like the, and, or, it …– may be compiled manually– may be derived automatically from a corpus of text

• any word that occurs in a relatively large percentage (>10-20%) of contexts may be considered a stop word


Lexical Features• Bigram

– an ordered pair of words that may be consecutive, or have intervening words that are ignored

– the pair occurs together more than X times and/or more often than expected by chance in feature selection data

– neither word in the pair may be in stop list

• Co-occurrence– an unordered bigram

• Target Co-occurrence– a co-occurrence where one of the words is the target


Bigrams

• Window Size of 2– baseball bat, fine wine, apple orchard, bill clinton

• Window Size of 3– house of representatives, bottle of wine,

• Window Size of 4– president of the republic, whispering in the wind

• Selected using a small window size (2-4 words)• Objective is to capture a regular or localized

pattern between two words (collocation?)


Co-occurrences

• president law– the president signed a bill into law today– that law is unjust, said the president– the president feels that the law was properly applied

• Usually selected using a larger window (7-10 words) of context, hoping to capture pairs of related words rather than collocations


Bigrams and Co-occurrences

• Pairs of words tend to be much less ambiguous than unigrams– “bank” versus “river bank” and “bank card”– “dot” versus “dot com” and “dot product”

• Three grams and beyond occur much less frequently (Ngrams very Zipfian)

• Unigrams occur more frequently, but are noisy


“occur together more often than expected by chance…”

• Observed frequencies for two words occurring together and alone are stored in a 2x2 matrix

• Expected values are calculated, based on the model of independence and observed values– How often would you expect these words to occur

together, if they only occurred together by chance?– If two words occur “significantly” more often than the

expected value, then the words do not occur together by chance.


2x2 Contingency Table

Intelligence not

Intelligence

Artificial 100 400

not

Artificial

300 100,000



Intelligence not

Intelligence

Artificial 100 300 400

not

Artificial

200 99,400 99,600

300 99,700 100,000



Intelligence not

Intelligence

Artificial 100.0

000.12

300.0

398.8

400

not

Artificial

200.0

298.8

99,400.0

99,301.2

99,600

300 99,700 100,000


Measures of Association

2

1,

22

2

1,

2

),(

)],(),([

)),(

),(log*),((

ji ji

jiji

ji

ji

jiji

wwexpected

wwexpectedwwobservedX

wwexpected

wwobservedwwobservedG



78.8191

88.7502

2

X

G


Interpreting the Scores…

• G^2 and X^2 are asymptotically approximated by the chi-squared distribution…

• This means…if you fix the marginal totals of a table, randomly generate internal cell values in the table, calculate the G^2 or X^2 scores for each resulting table, and plot the distribution of the scores, you *should* get …


Interpreting the Scores…

• Values above a certain level of significance can be considered grounds for rejecting the null hypothesis – H0: the words in the bigram are independent– 3.84 is associated with 95% confidence that

the null hypothesis should be rejected



• There are numerous measures of association that can be used to identify bigram and co-occurrence features

• Many of these are supported in the Ngram Statistics Package (NSP)– http://www.d.umn.edu/~tpederse/nsp.html

• NSP is integrated into SenseClusters

http://www.d.umn.edu/~tpederse/nsp.html


Measures Supported in NSP

• Log-likelihood Ratio (ll)• True Mutual Information (tmi)• Pointwise Mutual Information (pmi)• Pearson’s Chi-squared Test (x2)• Phi coefficient (phi)• Fisher’s Exact Test (leftFisher)• T-test (tscore)• Dice Coefficient (dice)• Odds Ratio (odds)


Summary• Identify lexical features based on frequency counts or

measures of association – either in the data to be clustered or in a separate set of feature selection data– Language independent

• Unigrams usually only selected by frequency– Remember, no labeled data from which to learn, so somewhat

less effective as features than in supervised case

• Bigrams and co-occurrences can also be selected by frequency, or better yet measures of association– Bigrams and co-occurrences need not be consecutive– Stop words should be eliminated– Frequency thresholds are helpful (e.g., unigram/bigram that

occurs once may be too rare to be useful)


References• Moore, 2004 (EMNLP) follow-up to Dunning and Pedersen on log-

likelihood and exact tests http://acl.ldc.upenn.edu/acl2004/emnlp/pdf/Moore.pdf

• Pedersen, Kayaalp, and Bruce. 1996 (AAAI) explanation of the exact conditional test, a stochastic simulation of exact tests. http://www.d.umn.edu/~tpederse/Pubs/aaai96-cmpl.pdf

• Pedersen, 1996 (SCSUG) explanation of exact tests for collocation identification, and comparison to log-likelihoodhttp://arxiv.org/abs/cmp-lg/9608010

• Dunning, 1993 (Computational Linguistics) introduces log-likelihood ratio for collocation identification

http://acl.ldc.upenn.edu/J/J93/J93-1003.pdf

http://acl.ldc.upenn.edu/acl2004/emnlp/pdf/Moore.pdf

http://www.d.umn.edu/~tpederse/Pubs/aaai96-cmpl.pdf

http://arxiv.org/abs/cmp-lg/9608010



Context Representations

First and Second Order Methods


Once features selected…

• We will have a set of unigrams, bigrams, co-occurrences or target co-occurrences that we believe are somehow interesting and useful– We also have any frequency and measure of

association score that have been used in their selection

• Convert contexts to be clustered into a vector representation based on these features


Possible Representations

• First Order Features– Native SenseClusters

• each context represented by a vectors of features

• Second Order Co-Occurrence Features– Native SenseClusters

• each word in a context replaced by vector of co-occurring words and averaged together

– Latent Semantic Analysis• each feature in a context replaced by vector of

contexts in which it occurs and averaged together


First Order RepresentationNative SenseClusters

• Context by Feature

• Each context is represented by a vector with M dimensions, each of which indicates if a particular feature occurred in that context– value may be binary or a frequency count– bag of words representation of documents is

first order, where each doc is represented by a vector showing words that occur therein


Contexts

• x1: there was an island curse of black magic cast by that voodoo child

• x2: harold a known voodoo child was gifted in the arts of black magic

• x3: despite their military might it was a serious error to attack

• x4: military might is no defense against a voodoo child or an island curse


Unigram Features

• island 1000• black 700• curse 500• magic 400• child 200

• (assume these are frequency counts obtained from feature selection data…)


First Order Vectors of Unigrams

island black curse magic child

x1 1 1 1 1 1

x2 0 1 0 1 1

x3 0 0 0 0 0

x4 1 0 1 0 1


Bigram Feature Set

• island curse 189.2• black magic 123.5• voodoo child 120.0• military might 100.3• serious error 89.2• island child 73.2• voodoo might 69.4• military error 54.9• black child 43.2• serious curse 21.2

• (assume these are log-likelihood scores from feature selection data)


First Order Vectors of Bigrams

black

magic

island curse

military might

serious error

voodoo child

x1 1 1 0 0 1

x2 1 0 0 0 1

x3 0 0 1 1 0

x4 0 1 1 0 1


First Order Vectors

• Values may be binary or frequency counts• Forms a context by feature matrix• May optionally be smoothed/reduced with

Singular Value Decomposition – More on that later…

• The contexts are ready for clustering…– More on that later…


Second Order Features

• First order features directly encode the occurrence of a feature in a context– Native SenseClusters : each feature represented by a

binary value or frequency count in a vector

• Second order features encode something ‘extra’ about a feature that occurs in a context, something not available in the context itself– Native SenseClusters : each feature is represented by a

vector of the words with which it occurs – Latent Semantic Analysis : each feature is represented

by a vector of the contexts in which it occurs


Second Order RepresentationNative SenseClusters

• Build word matrix from feature selection data– Start with bigrams or co-occurrences identified in

feature selection data– First word is row, second word is column, cell is

score– (optionally) reduce dimensionality w/SVD– Each row forms a vector of first order co-occurrences

• Replace each word in a context with its row from the word matrix

• Represent the context with the average of all its word vectors– Schütze (1998)


Word by Word Matrix

magic curse might error child

black 123.5 0 0 0 43.2

island 0 189.2 0 0 73.2

military 0 0 100.3 54.9 0

serious 0 21.2 0 89.2 0

voodoo 0 0 69.4 0 120.0


Word by Word Matrix

• …can also be used to identify sets of related words• In the case of bigrams, rows represent the first word in a

bigram and columns represent the second word– Matrix is asymmetric

• In the case of co-occurrences, rows and columns are equivalent– Matrix is symmetric

• The vector (row) for each word represent a set of first order features for that word

• Each word in a context to be clustered for which a vector exists (in the word by word matrix) is replaced by that vector in that context


There was an island curse of black magic cast by that voodoo child.


black 123.5 0 0 0 43.2

island 0 189.2 0 0 73.2

voodoo 0 0 69.4 0 120.0


Second Order Co-Occurrences

• Word vectors for “black” and “island” show similarity as both occur with “child”

• “black” and “island” are second order co-occurrence with each other, since both occur with “child” but not with each other (i.e., “black island” is not observed)


Second Order Representation

• x1: there was an island curse of black magic cast by that voodoo child

• x1: there was an [curse,child] curse of [magic, child] magic cast by that [might,child] child

• x1: [curse,child] + [magic,child] + [might,child]


There was an island curse of black magic cast by that voodoo child.


x1 41.2 63.1 24.4 0 78.8


Second Order RepresentationNative SenseClusters

• Context by Feature/Word

• Cell values do not indicate if feature occurred in context. Rather, they show the strength of association of that feature with other words that occur with a word in the context.


Second Order RepresentationLatent Semantic Analysis

• Build first order representation of context– Use any type of features selected from feature

selection data– result is a context by feature matrix

• Transpose the resulting first order matrix– result is a feature by context matrix– (optionally) reduce dimensionality w/SVD– Replace each feature in a context with its row from

the transposed matrix• Represent the context with the average of all its

context vectors– Landauer and Dumais (1997)


First Order Vectors of Unigrams

island black curse magic child

x1 1 1 1 1 1

x2 0 1 0 1 1

x3 0 0 0 0 0

x4 1 0 1 0 1


Transposed

x1 x2 x3 x4

island 1 0 0 1

black 1 1 0 0

curse 1 0 0 1

magic 1 1 0 0

child 1 1 0 1


harold a known voodoo child was gifted in the arts of black magic

x1 x2 x3 x4

black 1 1 0 0

child 1 1 0 1

magic 1 1 0 0


Second Order Representation

• x2: harold a known voodoo child was gifted in the arts of black magic

• x2: harold a known voodoo [x1,x2,x4] was gifted in the arts of [x1,x2] [x1,x2]

• x2: [x1,x2,x4] + [x1,x2] + [x1,x2]


x2: harold a known voodoo child was gifted in the arts of black magic

x1 x2 x3 x4

x2 1 1 0 .3


Second Order RepresentationLatent Semantic Analysis

• Context by Context

• The features in the context are represented by the contexts in which those features occur

• Cell values indicate the similarity between the contexts


Summary• First order representations are intuitive, but…

– Can suffer from sparsity– Contexts represented based on the features that occur in

those contexts• Second order representations are harder to visualize,

but…– Allow a word to be represented by the words it co-occurs

with (i.e., the company it keeps)– Allows a context to be represented by the words that occur

with the words in the context – Allow a feature to be represented by the contexts in which it

occurs– Allows a context to be represented by the contexts where

the words in the context occur– Helps combat sparsity…


References• Pedersen and Bruce 1997 (EMNLP) first order method of discrimination http://acl.ldc.upenn.edu/W/W97/W97-0322.pdf

• Landauer and Dumais 1997 (Psychological Review) overview of LSA. http://lsa.colorado.edu/papers/plato/plato.annote.html

• Schütze 1998 (Computational Linguistics) introduced second order method http://acl.ldc.upenn.edu/J/J98/J98-1004.pdf

• Purandare and Pedersen 2004 (CoNLL) compared first and second order methods

http://acl.ldc.upenn.edu/hlt-naacl2004/conll04/pdf/purandare.pdf

– First order better if you have lots of data– Second order better with smaller amounts of data

http://acl.ldc.upenn.edu/W/W97/W97-0322.pdf

http://www.d.umn.edu/~tpederse/Tutorials/


http://acl.ldc.upenn.edu/hlt-naacl2004/conll04/pdf/purandare.pdf


Dimensionality Reduction

Singular Value Decomposition


Motivation

• First order matrices are very sparse– Context by feature– Word by word

• NLP data is noisy– No stemming performed– synonyms


Many Methods

• Singular Value Decomposition (SVD)– SVDPACKC http://www.netlib.org/svdpack/

• Multi-Dimensional Scaling (MDS)

• Principal Components Analysis (PCA)

• Independent Components Analysis (ICA)

• Linear Discriminant Analysis (LDA)

• etc…

http://www.netlib.org/svdpack/


Effect of SVD

• SVD reduces a matrix to a given number of dimensions This may convert a word level space into a semantic or conceptual space– If “dog” and “collie” and “wolf” are

dimensions/columns in a word co-occurrence matrix, after SVD they may be a single dimension that represents “canines”


Effect of SVD

• The dimensions of the matrix after SVD are principal components that represent the meaning of concepts– Similar columns are grouped together

• SVD is a way of smoothing a very sparse matrix, so that there are very few zero valued cells after SVD


How can SVD be used?

• SVD on first order contexts will reduce a context by feature representation down to a smaller number of features– Latent Semantic Analysis performs SVD on a

feature by context representation, where the contexts are reduced

• SVD used in creating second order context representations for native SenseClusters– Reduce word by word matrix


Word by Word Matrixnative SenseClusters 2nd order

apple blood cells ibm data box tissue graphics memory organ plasma

pc 2 0 0 1 3 1 0 0 0 0 0

body 0 3 0 0 0 0 2 0 0 2 1

disk 1 0 0 2 0 3 0 1 2 0 0

petri 0 2 1 0 0 0 2 0 1 0 1

lab 0 0 3 0 2 0 2 0 2 1 3

sales 0 0 0 2 3 0 0 1 2 0 0

linux 2 0 0 1 3 2 0 1 1 0 0

debt 0 0 0 2 3 4 0 2 0 0 0


Singular Value DecompositionA=UDV’


U.35 .09 -.2 .52 -.09 .40 .02 .63 .20 -.00 -.02

.05 -.49 .59 .44 .08 -.09 -.44 -.04 -.6 -.02 -.01

.35 .13 .39 -.60 .31 .41 -.22 .20 -.39 .00 .03

.08 -.45 .25 -.02 .17 .09 .83 .05 -.26 -.01 .00

.29 -.68 -.45 -.34 -.31 .02 -.21 .01 .43 -.02 -.07

.37 -.01 -.31 .09 .72 -.48 -.04 .03 .31 -.00 .08

.46 .11 -.08 .24 -.01 .39 .05 .08 .08 -.00 -.01

.56 .25 .30 -.07 -.49 -.52 .14 -.3 -.30 .00 -.07


D9.19

6.36

3.99

3.25

2.52

2.30

1.26

0.66

0.00

0.00

0.00


V.21 .08 -.04 .28 .04 .86 -.05 -.05 -.31 -.12 .03

.04 -.37 .57 .39 .23 -.04 .26 -.02 .03 .25 .44

.11 -.39 -.27 -.32 -.30 .06 .17 .15 -.41 .58 .07

.37 .15 .12 -.12 .39 -.17 -.13 .71 -.31 -.12 .03

.63 -.01 -.45 .52 -.09 -.26 .08 -.06 .21 .08 -.02

.49 .27 .50 -.32 -.45 .13 .02 -.01 .31 .12 -.03

.09 -.51 .20 .05 -.05 .02 .29 .08 -.04 -.31 -.71

.25 .11 .15 -.12 .02 -.32 .05 -.59 -.62 -.23 .07

.28 -.23 -.14 -.45 .64 .17 -.04 -.32 .31 .12 -.03

.04 -.26 .19 .17 -.06 -.07 -.87 -.10 -.07 .22 -.20

.11 -.47 -.12 -.18 -.27 .03 -.18 .09 .12 -.58 .50


Word by Word Matrix After SVD

apple blood cells ibm data tissue graphics memory organ plasma

pc .73 .00 .11 1.3 2.0 .01 .86 .77 .00 .09

body .00 1.2 1.3 .00 .33 1.6 .00 .85 .84 1.5

disk .76 .00 .01 1.3 2.1 .00 .91 .72 .00 .00

germ .00 1.1 1.2 .00 .49 1.5 .00 .86 .77 1.4

lab .21 1.7 2.0 .35 1.7 2.5 .18 1.7 1.2 2.3

sales .73 .15 .39 1.3 2.2 .35 .85 .98 .17 .41

linux .96 .00 .16 1.7 2.7 .03 1.1 1.0 .00 .13

debt 1.2 .00 .00 2.1 3.2 .00 1.5 1.1 .00 .00


Second Order Co-Occurrences

• These two contexts share no words in common, yet they are similar! disk and linux both occur with “Apple”, “IBM”, “data”, “graphics”, and “memory”

• The two contexts are similar because they share many second order co-occurrences

apple blood cells ibm data tissue graphics memory organ Plasma

disk .76 .00 .01 1.3 2.1 .00 .91 .72 .00 .00

linux .96 .00 .16 1.7 2.7 .03 1.1 1.0 .00 .13

• I got a new disk today!

• What do you think of linux?


References

• Deerwester, S. and Dumais, S.T. and Furnas, G.W. and Landauer, T.K. and Harshman, R., Indexing by Latent Semantic Analysis, Journal of the American Society for Information Science, vol. 41, 1990

• Landauer, T. and Dumais, S., A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction and Representation of Knowledge, Psychological Review, vol. 104, 1997

• Schütze, H, Automatic Word Sense Discrimination, Computational Linguistics, vol. 24, 1998

• Berry, M.W. and Drmac, Z. and Jessup, E.R.,Matrices, Vector Spaces, and Information Retrieval, SIAM Review, vol 41, 1999


Clustering

Partitional Methods

Cluster Stopping

Cluster Labeling


Many many methods…

• Cluto supports a wide range of different clustering methods– Agglomerative

• Average, single, complete link…

– Partitional• K-means (Direct)

– Hybrid• Repeated bisections

• SenseClusters integrates with Cluto– http://www-users.cs.umn.edu/~karypis/cluto/

http://www-users.cs.umn.edu/~karypis/cluto/


General Methodology

• Represent contexts to be clustered in first or second order vectors

• Cluster the context vectors directly– vcluster

• … or convert to similarity matrix and then cluster– scluster


Agglomerative Clustering

• Create a similarity matrix of contexts to be clustered– Results in a symmetric “instance by instance”

matrix, where each cell contains the similarity score between a pair of instances

– Typically a first order representation, where similarity is based on the features observed in the pair of instances

)(

)(

YX

YX


Measuring Similarity

• Integer Values

– Matching Coefficient

– Jaccard Coefficient

– Dice Coefficient

• Real Values

– Cosine

YX

YX

YX

YX

YX

2

YX

YX


Agglomerative Clustering

• Apply Agglomerative Clustering algorithm to similarity matrix– To start, each context is its own cluster– Form a cluster from the most similar pair of contexts– Repeat until the desired number of clusters is

obtained

• Advantages : high quality clustering • Disadvantages – computationally expensive,

must carry out exhaustive pair wise comparisons


Average Link Clustering

S1 S2 S3 S4

S1 3 4 2

S2 3 2 0

S3 4 2 1

S4 2 0 1

S1S3 S2 S4

S1S3

S2 0

S4 0

5.22

23

5.22

23

5.12

12

5.12

12

S1S3S2

S4

S1S3S2

S4

5.12

5.15.1

5.12

5.15.1


Partitional Methods

• Randomly create centroids equal to the number of clusters you wish to find

• Assign each context to nearest centroid• After all contexts assigned, re-compute

centroids– “best” location decided by criterion function

• Repeat until stable clusters found– Centroids don’t shift from iteration to iteration


Partitional Methods

• Advantages : fast

• Disadvantages– Results can be dependent on the initial

placement of centroids– Must specify number of clusters ahead of time

• maybe not…


Vectors to be clustered


Random Initial Centroids (k=2)


Assignment of Clusters


Recalculation of Centroids


Reassignment of Clusters


Recalculation of Centroid


Reassignment of Clusters


Partitional Criterion Functions

• Intra-Cluster (Internal) similarity/distance– How close together are members of a cluster?– Closer together is better

• Inter-Cluster (External) similarity/distance– How far apart are the different clusters?– Further apart is better


Intra Cluster Similarity

• Ball of String (I1)– How far is each member from each other

member

• Flower (I2)– How far is each member of cluster from

centroid


Contexts to be Clustered


Ball of String (I1 Internal Criterion Function)


Flower(I2 Internal Criterion Function)


Inter Cluster Similarity

• The Fan (E1)– How far is each centroid from the centroid of

the entire collection of contexts– Maximize that distance


The Fan(E1 External Criterion Function)


Hybrid Criterion Functions

• Balance internal and external similarity– H1 = I1/E1– H2 = I2/E1

• Want internal similarity to increase, while external similarity decreases

• Want internal distances to decrease, while external distances increase


Cluster Stopping


Cluster Stopping

• Many Clustering Algorithms require that the user specify the number of clusters prior to clustering

• But, the user often doesn’t know the number of clusters, and in fact finding that out might be the goal of clustering


Criterion Functions Can Help

• Run partitional algorithm for k=1 to deltaK– DeltaK is a user estimated or automatically

determined upper bound for the number of clusters

• Find the value of k at which the criterion function does not significantly increase at k+1

• Clustering can stop at this value, since no further improvement in solution is apparent with additional clusters (increases in k)


H2 versus kT. Blair – V. Putin – S. Hussein


PK2

• Based on Hartigan, 1975• When ratio approaches 1, clustering is at a plateau• Select value of k which is closest to but outside of

standard deviation interval

)1(2

)(2)(2

kH

kHkPK


PK2 predicts 3 sensesT. Blair – V. Putin – S. Hussein


PK3• Related to Salvador and Chan, 2004• Inspired by Dice Coefficient• Values close to 1 mean clustering is improving …• Select value of k which is closest to but outside of

standard deviation interval

)1(2)1(2

)(2*2)(3

kHkH

kHkPK


PK3 predicts 3 sensesT. Blair – V. Putin – S. Hussein


Adapted Gap Statistic• Gap Statistic by Tibshirani et al. (2001)• Cluster stopping by comparing observed data to

randomly generated data – Fix marginal totals of observed data, generate

random matrices– Random matrices should have 1 cluster, since there

is no structure to the data– Compare criterion function of observed data to

random data– The point where the difference between criterion

function is greatest is the point where the observed data is least like noise (and is where we should stop)


Adapted Gap Statistic


Gap predicts 3 sensesT. Blair – V. Putin – S. Hussein


References• Hartigan, J. Clustering Algorithms, Wiley, 1975

– basis for SenseClusters stopping method PK2• Mojena, R., Hierarchical Grouping Methods and Stopping Rules: An

Evaluation, The Computer Journal, vol 20, 1977 – basis for SenseClusters stopping method PK1

• Milligan, G. and Cooper, M., An Examination of Procedures for Determining the Number of Clusters in a Data Set, Psychometrika, vol. 50, 1985– Very extensive comparison of cluster stopping methods

• Tibshirani, R. and Walther, G. and Hastie, T., Estimating the Number of Clusters in a Dataset via the Gap Statistic,Journal of the Royal Statistics Society (Series B), 2001

• Pedersen, T. and Kulkarni, A. Selecting the "Right" Number of Senses Based on Clustering Criterion Functions, Proceedings of the Posters and Demo Program of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics, 2006– Describes SenseClusters stopping methods


Cluster Labeling


Cluster Labeling

• Once a cluster is discovered, how can you generate a description of the contexts of that cluster automatically?

• In the case of contexts, you might be able to identify significant lexical features from the contents of the clusters, and use those as a preliminary label


Results of Clustering

• Each cluster consists of some number of contexts

• Each context is a short unit of text• Apply measures of association to the

contents of each cluster to determine N most significant bigrams

• Use those bigrams as a label for the cluster


Label Types

• The N most significant bigrams for each cluster will act as a descriptive label

• The M most significant bigrams that are unique to each cluster will act as a discriminating label


George Miller Labels

• Cluster 0 : george miller, delay resignation, tom delay, 202 2252095, 2205 rayburn,, constituent services, bethel high, congressman george, biography constituent

• Cluster 1 : george miller, happy feet, pig in, lorenzos oil, 1998 babe, byron kennedy, babe pig, mad max

• Cluster 2 : george a, october 26, a miller, essays in, mind essays, human mind


Evaluation Techniques

Comparison to gold standard data


Evaluation

• If Sense tagged text is available, can be used for evaluation– But don’t use sense tags for clustering or

feature selection!

• Assume that sense tags represent “true” clusters, and compare these to discovered clusters– Find mapping of clusters to senses that

attains maximum accuracy


Evaluation

• Pseudo words are especially useful, since it is hard to find data that is discriminated– Pick two words or names from a corpus, and

conflate them into one name. Then see how well you can discriminate.

– http://www.d.umn.edu/~tpederse/tools.html

• Baseline Algorithm– group all instances into one cluster, this will reach “accuracy” equal to majority classifier


Evaluation

• Pseudo words are especially useful, since it is hard to find data that is discriminated– Pick two or more words or names from a

corpus, and conflate them into one name. Then see how well you can discriminate.

– http://www.d.umn.edu/~tpederse/tools.html


Baseline Algorithm

• Baseline Algorithm – group all instances into one cluster, this will reach “accuracy” equal to majority classifier

• What if the clustering said everything should be in the same cluster?


Baseline Performance

S1 S2 S3 Totals

C1 0 0 0 0

C2 0 0 0 0

C3 80 35 55 170

Totals 80 35 55 170

S3 S2 S1 Totals

C1 0 0 0 0

C2 0 0 0 0

C3 55 35 80 170

Totals 55 35 80 170

(0+0+55)/170 = .32 if C3 is S1 (0+0+80)/170 = .47 if C3 is S3


Evaluation• Suppose that C1 is labeled S1, C2 as S2, and C3 as S3• Accuracy = (10 + 0 + 10) / 170 = 12% • Diagonal shows how many members of the cluster actually belong to

the sense given on the column • Can the “columns” be rearranged to improve the overall accuracy?

– Optimally assign clusters to senses

S1 S2 S3 Totals

C1 10 30 5 45

C2 20 0 40 60

C3 50 5 10 65

Totals 80 35 55 170


Evaluation

• The assignment of C1 to S2, C2 to S3, and C3 to S1 results in 120/170 = 71%

• Find the ordering of the columns in the matrix that maximizes the sum of the diagonal.

• This is an instance of the Assignment Problem from Operations Research, or finding the Maximal Matching of a Bipartite Graph from Graph Theory.

S2 S3 S1 Totals

C1 30 5 10 45

C2 0 40 20 60

C3 5 10 50 65

Totals 35 55 80 170


Analysis

• Unsupervised methods may not discover clusters equivalent to the classes learned in supervised learning

• Evaluation based on assuming that sense tags represent the “true” cluster are likely a bit harsh. Alternatives?– Humans could look at the members of each cluster and

determine the nature of the relationship or meaning that they all share

– Use the contents of the cluster to generate a descriptive label that could be inspected by a human


Hands on Experience

Experiments with SenseClusters


Things to Try

• Feature Identification– Type of Feature– Measures of association

• Context Representation – native SenseClusters (1st and 2nd order)– Latent Semantic Analysis (2nd order)

• Automatic Stopping (or not)• SVD (or not)• Evaluation• Labeling


Experimental Data

• Available on Web Site– http://senseclusters.sourceforge.net

• Available on LIVE CD

• Mostly “Name Conflate” data



Creating Experimental Data

• NameConflate program– Creates name conflated data from English

GigaWord corpus

• Text2Headless program – Convert plain text into headless contexts

• http://www.d.umn.edu/~tpederse/tools.html

http://www.d.umn.edu/~tpederse/tools.html


Thank you!

• Questions or comments on tutorial or SenseClusters are welcome at any time

[email protected]

• SenseClusters is freely available via LIVE CD, the Web, and in source code form

http://senseclusters.sourceforge.net

• SenseClusters papers available at:http://www.d.umn.edu/~tpederse/senseclusters-pubs.html

mailto:[email protected]


http://www.d.umn.edu/~tpederse/senseclusters-pubs.html


Target Word ClusteringSenseClusters Native Mode

• line data – 6 manually determined senses– approx. 4,000 contexts

• second order bigram features– selected with pmi– use SVD– word by word co-occurrence matrix

• cluster stopping– all methods


Target Word ClusteringLatent Semantic Analysis


• second order bigram features– selected with pmi– use SVD– bigram by context matrix



Feature ClusteringLatent Semantic Analysis


• first order bigram features– selected with pmi– use SVD– bigram by context matrix


Ijcai 2007 Pedersen

Technology

Transcript of Ijcai 2007 Pedersen