representing meaning in unsupervised word sense disambiguation

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Representing Meaning in Unsupervised Word Sense

Disambiguation

Bridget T. McInnes

5 September 2008

University of Minnesota Twin Cities

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What is WSD?

The culture count doubled.

Culture

LaboratoryCulture

AnthropologicalCulture

Sense Inventory

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Approaches to WSD

SupervisedAdvantages: obtains a high accuracyDisadvantages: manually annotated training data is required for each word that needs to be disambiguated therefore it can not scale

UnsupervisedAdvantages: does not require manually annotated training dataDisadvantages: generally does not obtain as high of an accuracy as supervised approaches

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Unsupervised Approaches

Similarity and Relatedness Based

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Similarity and Relatedness BasedPatwardhan, Banerjee and Pedersen 2005Pedersen, et al 2006Budanitsky and Hirst 2006

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Similarity and Relatedness based

Vector Based

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Vector-basedMohammad and Hirst, 2006Patwardhan, 2003Pedersen, et al 2006Humphrey, et al 2006

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Similarity and Relatedness-based

Vector-based

Clustering

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Similarity and Relatedness based

Vector-based

ClusteringPedersen and Bruce, 1997Shütze, 1998Pedersen and Bruce, 1998Purandare and Pedersen, 2004Kulkarni and Pedersen, 2005

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Road Map

Previous Approaches

Our vector approach

Future Work

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Previous Approaches


SenseRelate (Banerjee and Pedersen, 2003)

Vector-based

Semantic Type Indexing (Humphrey et al 2006)

Clustering

SenseClusters (Kulkarni and Pedersen, 2005)

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Banerjee and Pedersen 2003

Sense Relate

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SenseRelateTarget Word: Transport

Concept 1: Biological Transport (C0005528)

Concept 2: Patient Transport (C0150390)

Transport of glutathione S-linked conjugates.

glutathione S-linked conjugates.

C0017817C0522529 C0301869

C0005528 = SS + SS + SS = Total SS for Concept 1

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SenseRelateTarget Word: Transport




glutathione S-linked conjugates.

C0017817C0522529 C0301869

C0150390 = SS + SS + SS = Total SS for concept 2

C0005528 = SS + SS + SS = Total SS for concept 1

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Humphrey et al, 2006

Semantic Type Indexing for WSD

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Semantic Type Indexing (STI) Target Word: Transport

Concept 2 Vector

Concept 1 Vector

Target Word VectorCosine 2

Cosine 1

Concept 1: Biological TransportSemantic type: Cell Function

Concept 2: Patient TransportSemantic type: Health Care Activity

JDI

CV1 – JDI vectorCV2 – JDI vector

TW – JDI vector


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Target Word Vector


Contains the words surrounding the ambiguous word

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STI - Target Word Vectors



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STI -Concept Vectors

The concept vectors are created based on their semantic type(s)

Transport:C0005528: Biological TransportC0150390: Patient Transport

C0005528

C0150390

Cell FunctionOne word terms in the Metathesaurus associated with Cell Function

Health Care Activity One word terms in the Metathesaurus associated with Health Care Activity

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Kulkarni and Pedersen, 2005

SenseClusters

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Sense Clusters (SC)Target Word: Transport

Concept 1: Biological TransportConcept 2: Patient Transport

Instance 1Instance 2Instance 3Instance 4Instance 5Instance 6Instance 7Instance 8Instance 9Instance 10Instance 11Instance 12Instance 13…

Concept 1

Concept 2


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Sense Clusters (SC)

Instance 1Instance 2Instance 3Instance 4Instance 5Instance 6Instance 7Instance 8Instance 9Instance 10Instance 11Instance 12Instance 13…

Concept 1

Concept 2

Target Word: Transport



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Sense Clusters

Concept 2 Vector

Concept 1 Vector

Target Word Vector

Cosine 2

Cosine 1




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SC -Vectors

Contain the words surrounding the ambiguous word

Created using:

First order co-occurrences

Second order co-occurrences

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First Order Co-occurrence Vectors

glutathione S-linked conjugates

Word 1

Word 2

Word N

.

.

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.

50

6

5

.

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5

6

1

.

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5

0

15

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20

4

7

TargetVector

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Second Order Co-occurrence Vectors

Word 1

Word 2

Word N

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.

.

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.

10

30

0

1st orderglutathione

20 10 0

10

0

0

2

50

2

…

…

…

…

…

… …

Word1 Word 2 … Word N

0 2 2…

2nd orderglutathione

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S-linked conjugates

Word 1

Word 2

Word N

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10

30

2

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0

6

0

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5

0

13

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.

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5

13

5

TargetVector

glutathione

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Our unsupervised approach

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CuiTools ApproachOur approach uses a general vector approach with SenseCluster vectors

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CuiTools

Concept 2 Vector

Concept 1 Vector

Target Word Vector

Cosine 2

Cosine 1





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CuiTools Approach

We explore using

First-order co-occurrence vectors

Second-order co-occurrence vectors

Our approach uses a general vector approach with SenseCluster vectors

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Target Word Vector



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CuiTools - Concept Vectors

How to create a vector that can represent the meaning of

a concept for word sense disambiguation?

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To answer this question

We explore information in the UMLS that can be used to

represent the meaning of a concept.

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Adjustment

Individual AdjustmentConceptually broad term referring to a state of harmony between internal needs and external …

Adjustment ActionThe act of making necessary corrections or modifications …

Psychological AdjustmentA state of harmony between internal needs and external demands and the processes used …

CUI definition

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Blood Pressure

Blood PressureForce exerted by the blood on the walls of the arteries and other vessels.

Blood Pressure DeterminationActions performed to measure the diastolic and systolic pressure of the blood.

Arterial PressureNO DEFINTION

CUI definition

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CUI definitionUse CUI definition but if it doesn’t exist

PARent definitionSemantic Type definition

SYNonymous terms

For example:C0430400: Laboratory Culture

laboratory culturemicrobial culturesample culture

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CUI definition


SIBlings

For example:C0010453: Anthropological Culture

archeologyfamilysocial groups

If CUI definition doesn’t exist

SYNonymous terms

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CUI definitionIf CUI definition doesn’t exist


SIBlings

SYNonymous terms

TOP 50 most frequent words surrounding the terms associated with the CUI

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Dataset

National Library of Medicine's Word Sense Disambiguation (NLM-WSD) Dataset

50 words from the 1998 MEDLINE abstracts

100 instances for each of the 50 words

The target word was manually assigned a UMLS concept or None

All instances of None were removed

Average number of concepts per ambiguous word is 2.26

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Data subsets

Humphrey subset

Humphrey, et al 2006

45 out of the 50 words in NLM-WSD

5 words were excluded because at least two of the possible concepts associated with these words have the same semantic type

Instances that were assigned “None” were removed

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Training Data

The training data used to create the 1st and 2nd order co-occurrence vectors is

2005 Medline baseline

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Results

Results

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Results of Co-occurrence Vectors

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Results of the Representations of Meaning

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Results of the Representations of Meaning - CUI

Adding the parent and semantic type definitions decreased the accuracy by 6 and 7 percentage points

Parent and semantic type definitions are too broad to define the meaning of a concept

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Results of the Representations of Meaning - SYN

Using the synonymous terms associated with a concept is too narrow to represent the meaning.

Adjustment ActionAdjustment – actionAdjustmentsAdjustment, NOSAdjustment – action qualifier valueAdjustment – action procedure

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Results of the Representations of Meaning - SIB

Using the terms associated the siblings of a concept is too broad to represent the meaning.

Adjustment ActionBiopsyCauterisationCauteryCold TherapyDesiccationDrainage procedureElectrolysis

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Results of the Representations of Meaning

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Supervised versus Unsupervised

Joshi McInnes Stevenson SenseClusters Humphrey CuiTools et al 04 et al 07 et al 08 et al 06

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To recap

How to create a vector that can represent the meaning of

a concept for word sense disambiguation?

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Conclusions

To answer this we explored information in the UMLS that could be used to represent the meaning of a concept

Finding a context to represent the meaning of a concept is difficult

We found using the top 50 most frequent words surrounding the terms associated with the concept best represented the concept for the task of word sense disambiguation

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Take away message

Unsupervised approaches are showing promise

Their disadvantage due to supervised approaches obtaining a higher disambiguation accuracy is slowly disappearing

But we are not there yet … so there is more work to do

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Future Work

UMLS-Similarity package

Using the Semantic Similarity scores rather than frequency in the 1st order co-occurrence vectors

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Word 1

Word 2

Word N

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50

6

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5

6

1

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5

0

15

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20

4

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TargetVector

FREQ (glutathione, word N) Average

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Word 1

Word 2

Word N

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.5

.6

.5

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.5

.6

.1

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.5

0

.15

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.75

.6

.25

TargetVector

Similarity (glutathione, word N) Average

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Word 1

Word 2

Word N

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.5

.6

.5

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.5

.6

.1

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.5

0

.15

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1.5

1.2

.75

TargetVector

Similarity (glutathione, word N) Sum (like SenseRelate)

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First Order Co-occurrences

glutathione

Word 1

Word 2

Word N

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.5

.6

.5

Word N

(C0005528)

.3+ .2

C0000000 C0000001

Similarity = = .5

C0005528

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Future Work


Creating 2nd order co-occurrence matrices based on highly similar concepts rather than words in text


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Word 1

Word 2

Word N

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.

20 10 0

10

0

0

2

50

2

…

…

…

…

…

… …

Word1 Word 2 … Word N

Words come from training corpus

Frequency counts

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CUI 1

CUI 2

CUI N

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.

.

.

.20 .10 0

.10

0

0

.20

.50

.20

…

…

…

…

…

… …

CUI1 CUI2 … CUI N

Use concepts from the UMLS

Similarity scores

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Future Work


Creating 2nd order co-occurrence matrices based on highly similar concepts rather than co-occurrences in text

Use terms associated with CUIs that have a high similarity score with the possible concept to represent the meaning of the concept


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Similarity Scores

What is potentially gained by using the similarity or relatedness measures

May catch words/concepts that are similar but do not frequently occur together in the training data

culture and ethnology

Ethnology is the study of anthropology

ethnology appears with culture only five times in the training data

The concepts Anthropological Culture and Ethnology would have a high similarity score where as Laboratory culture and Ethnology would not

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Software

CuiTools version 0.19

http://cuitools.sourceforge.net

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Thank you

Lan AronsonFrançois LangJim MorkAurélie NévéolWill Rogers

Olivier BodenreiderAllen BrowneMay CheyDina Demner-FushmanGuy DivitaKin Wah FungSusanne HumphreyDwayne McCullyTom RindfleschSuresh Srinivasan

representing meaning in unsupervised word sense disambiguation

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