Advances In Wsd Acl 2005

Advances in Word Sense Disambiguation Tutorial at ACL 2005 June 25, 2005 Ted Pedersen University of Minnesota, Duluth http://www.d.umn.edu/~tpederse Rada Mihalcea University of North Texas http://www.cs.unt.edu/~rada

Goal of the Tutorial ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Outline of Tutorial ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]

Definitions ,[object Object],[object Object],[object Object],[object Object],[object Object]

Computers versus Humans ,[object Object],[object Object],[object Object]

Ambiguity for Humans - Newspaper Headlines! ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Ambiguity for a Computer ,[object Object],[object Object],[object Object],[object Object],[object Object]

Early Days of WSD ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Since then… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Interdisciplinary Connections ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Practical Applications ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Overview of the Problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Word Senses ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Approaches to Word Sense Disambiguation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

All Words Word Sense Disambiguation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

All Words Word Sense Disambiguation ,[object Object],[object Object],[object Object],[object Object],[object Object]

Targeted Word Sense Disambiguation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Targeted Word Sense Disambiguation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Unsupervised Disambiguation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Unsupervised Disambiguation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Evaluating Word Sense Disambiguation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Evaluating Word Sense Disambiguation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Bounds on Performance ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

References ,[object Object],[object Object],[object Object],[object Object]

Part 3: Knowledge-based Methods for Word Sense Disambiguation

Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Task Definition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Machine Readable Dictionaries ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

MRD – A Resource for Knowledge-based WSD ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

MRD – A Resource for Knowledge-based WSD ,[object Object],[object Object],[object Object],[object Object],WordNet synsets for the noun “plant” 1. plant, works, industrial plant 2. plant, flora, plant life WordNet related concepts for the meaning “plant life” {plant, flora, plant life} hypernym: {organism, being} hypomym: {house plant}, {fungus}, … meronym: {plant tissue}, {plant part} holonym: {Plantae, kingdom Plantae, plant kingdom}

Lesk Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Pine#1  Cone#1 = 0 Pine#2  Cone#1 = 0 Pine#1  Cone#2 = 1 Pine#2  Cone#2 = 0 Pine#1  Cone#3 = 2 Pine#2  Cone#3 = 0

Lesk Algorithm for More than Two Words? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Lesk Algorithm: A Simplified Version ,[object Object],[object Object],[object Object],[object Object],[object Object]

Lesk Algorithm: A Simplified Version ,[object Object],[object Object],[object Object],[object Object],[object Object],Pine#1  Sentence = 1 Pine#2  Sentence = 0 ,[object Object],[object Object],[object Object],[object Object]

Evaluations of Lesk Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Selectional Preferences ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Acquiring Selectional Preferences ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Preliminaries: Learning Word-to-Word Relations ,[object Object],[object Object],[object Object],[object Object],[object Object]

Learning Selectional Preferences (1) ,[object Object],[object Object],[object Object]

Learning Selectional Preferences (2) ,[object Object],[object Object],[object Object],[object Object],[object Object]

Learning Selectional Preferences (3) ,[object Object],[object Object],[object Object],[object Object],[object Object]

Using Selectional Preferences for WSD ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Given the selectional preference “DRINK BEVERAGE” : coffee#1

Evaluation of Selectional Preferences for WSD ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Semantic Similarity ,[object Object],[object Object],[object Object],[object Object],[object Object]

Semantic Similarity in a Local Context ,[object Object],[object Object],[object Object],carnivore wild dog wolf bear feline, felid canine, canid fissiped mamal, fissiped dachshund hunting dog hyena dog dingo hyena dog terrier

Semantic Similarity Metrics (1) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],, D is the taxonomy depth

Semantic Similarity Metrics (2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Semantic Similarity Metrics for WSD ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Semantic Similarity in a Global Context ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Semantic Similarity of a Global Context A very long train traveling along the rails with a constant velocity v in a certain direction … train #1: public transport #2: order set of things #3: piece of cloth travel #1 change location #2: undergo transportation rail #1: a barrier # 2: a bar of steel for trains #3: a small bird

Lexical Chains for WSD ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Most Frequent Sense (1) ,[object Object],[object Object],[object Object],[object Object],[object Object]

Most Frequent Sense (2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Most Frequent Sense(3) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

One Sense Per Discourse ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

One Sense per Collocation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Part 4: Supervised Methods of Word Sense Disambiguation

What is Supervised Learning? ,[object Object],[object Object],[object Object],[object Object]

Learn from these examples : “when do I go to the store?” YES NO NO NO 4 NO NO NO YES 3 YES NO YES NO 2 NO NO YES YES 1 F3 Ate Well? F2 Slept Well? F1 Hot Outside? CLASS Go to Store? Day

Task Definition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Sense Tagged Text My bank/1 charges too much for an overdraft. The University of Minnesota has an East and a West Bank/2 campus right on the Mississippi River. My grandfather planted his pole in the bank/2 and got a great big catfish! The bank/2 is pretty muddy, I can’t walk there. I went to the bank/1 to deposit my check and get a new ATM card. Bonnie and Clyde are two really famous criminals, I think they were bank/1 robbers

Two Bags of Words (Co-occurrences in the “window of context”) RIVER_BANK_BAG: a an and big campus cant catfish East got grandfather great has his I in is Minnesota Mississippi muddy My of on planted pole pretty right River The the there University walk West FINANCIAL_BANK_BAG: a an and are ATM Bonnie card charges check Clyde criminals deposit famous for get I much My new overdraft really robbers the they think to too two went were

Simple Supervised Approach ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Supervised Methodology ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

From Text to Feature Vectors ,[object Object],[object Object],FINANCE Y N Y N det verb det S2 SHORE N Y N Y det prep det adv S1 SENSE TAG interest river check fish P+2 P+1 P-1 P-2

Supervised Learning Algorithms ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Naïve Bayesian Classifier ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Bayesian Inference ,[object Object],[object Object],[object Object],[object Object]

The Naïve Bayesian Classifier ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Comparative Results ,[object Object],[object Object],[object Object],[object Object]

Decision Lists and Trees ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Decision List for WSD (Yarowsky, 1994) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Building the Decision List ,[object Object],[object Object]

Computing DL score ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Using the Decision List ,[object Object],N/A of the bank 0.00 Bank/2 river pole within bank 1.09 Bank/2 river bank is muddy 2.20 Bank/1 financial credit within bank 3.89 Sense Feature DL-score

Learning a Decision Tree ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Supervised WSD with Individual Classifiers ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Convergence of Results ,[object Object],[object Object],[object Object],[object Object],[object Object]

Ensembles of Classifiers ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Ensemble Considerations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Ensemble Results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Part 5: Minimally Supervised Methods for Word Sense Disambiguation

Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Task Definition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Bootstrapping WSD Classifiers ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Bootstrapping Recipe ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

… plants#1 and animals … … industry plant#2 … … building the only atomic plant … … plant growth is retarded … … a herb or flowering plant … … a nuclear power plant … … building a new vehicle plant … … the animal and plant life … … the passion-fruit plant … Classifier 1 Classifier 2 … plant#1 growth is retarded … … a nuclear power plant#2 …

Co-training / Self-training ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Co-training

Self-training ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Parameter Setting for Co-training/Self-training ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Experiments with Co-training / Self-training for WSD ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Parameter Settings ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Yarowsky Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Learning Algorithm ,[object Object],[object Object],[object Object],… ... ... ,[object Object],plant species 9.02 ,[object Object],fruit (within +/- k words) 9.03 ,[object Object],job (within +/- k words) 9.24 ,[object Object],flower (within +/- k words) 9.31 … … … Sense Collocation LogL

Bootstrapping Algorithm ,[object Object],[object Object],Sense-B: factory Sense-A: life

Bootstrapping Algorithm ,[object Object]

Bootstrapping Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Evaluation ,[object Object],[object Object],[object Object],[object Object],Unsupervised Bootstrapping 96.5 92.2 96.1 - Avg. … - … … … Supervised 97.9 92 98.0 legal/physical motion 96.5 95 97.1 vehicle/container tank 93.6 90 93.9 volume/outer space 98.6 92 97.7 living/factory plant Unsupervised Sch ü tze Senses Word

The Web as a Corpus ,[object Object],[object Object],[object Object],[object Object],[object Object]

Monosemous Relatives ,[object Object],[object Object],[object Object],[object Object],[object Object],As a pastime , she enjoyed reading. Evaluate the interestingness of the website. As an interest , she enjoyed reading. Evaluate the interest of the website.

Heuristics to Identify Monosemous Relatives ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Heuristics to Identify Monosemous Relatives

Example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Experimental Evaluation ,[object Object],[object Object],[object Object],[object Object]

Web-based Bootstrapping ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

The Web as Collective Mind ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

OMWE online http://teach-computers.org

Open Mind Word Expert: Quantity and Quality ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Part 6: Unsupervised Methods of Word Sense Disambiguation

What is Unsupervised Learning? ,[object Object],[object Object],[object Object],[object Object],[object Object]

Cluster this Data! Facts about my day… YES NO NO 4 NO NO NO 3 YES NO YES 2 NO NO YES 1 F3 Ate Well? F2 Slept Well? F1 Hot Outside? Day

Cluster this Data! YES NO NO 4 NO NO NO 3 YES NO YES 2 NO NO YES 1 F3 Ate Well? F2 Slept Well? F1 Hot Outside? Day

Task Definition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Task Definition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Agglomerative Clustering ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Measuring Similarity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Instances to be Clustered N N N Y det verb adj det S3 Y N Y N det prep det S2 N N N N noun noun noun det S4 N Y N Y det prep det adv S1 interest river check fish P+2 P+1 P-1 P-2 1 2 4 S3 1 2 4 S3 0 2 S4 0 3 S2 2 3 S1 S4 S2 S1

Average Link Clustering aka McQuitty’s Similarity Analysis 1 2 4 S3 1 2 4 S3 0 2 S4 0 3 S2 2 3 S1 S4 S2 S1 0 S4 0 S2 S1S3 S4 S2 S1S3 S4 S1S3S2 S4 S1S3S2

Evaluation of Unsupervised Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Baseline Performance ,[object Object],170 55 35 80 Totals 170 55 35 80 C3 0 0 0 0 C2 0 0 0 0 C1 Totals S3 S2 S1 170 80 35 55 Totals 170 80 35 55 C3 0 0 0 0 C2 0 0 0 0 C1 Totals S1 S2 S3

Evaluation ,[object Object],[object Object],[object Object],[object Object],[object Object],170 55 35 80 Totals 65 10 5 50 C3 60 40 0 20 C2 45 5 30 10 C1 Totals S3 S2 S1

Evaluation ,[object Object],[object Object],[object Object],170 80 55 35 Totals 65 50 10 5 C3 60 20 40 0 C2 45 10 5 30 C1 Totals S1 S3 S2

Agglomerative Approach ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Experimental Evaluation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Analysis ,[object Object],[object Object],[object Object],[object Object],[object Object]

Latent Semantic Indexing/Analysis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Co-occurrence matrix 4 2 0 0 0 3 0 1 box 0 1 2 2 1 2 0 0 memory 0 0 0 1 0 0 2 0 organ 0 2 0 3 2 0 0 0 debt 0 1 0 3 1 0 0 2 linux 0 1 0 3 2 0 0 0 sales 3 0 2 2 0 3 0 0 lab 1 0 2 0 0 1 2 0 petri 0 1 0 0 2 0 0 1 disk 1 0 2 0 0 0 3 0 body 0 0 0 3 1 0 0 2 pc plasma graphics tissue data ibm cells blood apple

Singular Value Decomposition A=UDV’

U -.52 .39 -.48 .02 .09 .41 -.09 .40 -.30 .08 .31 .43 -.26 -.39 -.6 .20 .00 -.00 -.00 -.02 -.01 .00 -.02 -.00 -.07 -.3 .14 -.49 -.07 .30 .25 .56 -.01 .08 .05 -.01 .24 -.08 .11 .46 .08 .03 -.04 .72 .09 -.31 -.01 .37 -.07 .01 -.21 -.31 -.34 -.45 -.68 .29 .00 .05 .83 .17 -.02 .25 -.45 .08 .03 .20 -.22 .31 -.60 .39 .13 .35 -.01 -.04 -.44 .08 .44 .59 -.49 .05 -.02 .63 .02 -.09 .52 -.2 .09 .35

D 0.00 0.00 0.00 0.66 1.26 2.30 2.52 3.25 3.99 6.36 9.19

V -.20 .22 -.07 -.10 -.87 -.07 -.06 .17 .19 -.26 .04 .03 .17 -.32 .02 .13 -.26 -.17 .06 -.04 .86 .50 -.58 .12 .09 -.18 -.27 -.18 -.12 -.47 .11 -.03 .12 .31 -.32 -.04 .64 -.45 -.14 -.23 .28 .07 -.23 -.62 -.59 .05 .02 -.12 .15 .11 .25 -.71 -.31 -.04 .08 .29 -.05 .05 .20 -.51 .09 -.03 .12 .31 -.01 .02 -.45 -.32 .50 .27 .49 -.02 .08 .21 -.06 .08 -.09 .52 -.45 -.01 .63 .03 -.12 -.31 .71 -.13 .39 -.12 .12 .15 .37 .07 .58 -.41 .15 .17 -.30 -.32 -.27 -.39 .11 .44 .25 .03 -.02 .26 .23 .39 .57 -.37 .04 .03 -.12 -.31 -.05 -.05 .04 .28 -.04 .08 .21

Co-occurrence matrix after SVD 1.1 1.0 .98 1.7 .86 .72 .85 .77 memory .00 .00 .17 1.2 .77 .00 .84 .00 organ .00 1.5 .00 3.2 2.1 .00 .00 1.2 debt .13 1.1 .03 2.7 1.7 .16 .00 .96 linux .41 .85 .35 2.2 1.3 .39 .15 .73 sales 2.3 .18 2.5 1.7 .35 2.0 1.7 .21 lab 1.4 .00 1.5 .49 .00 1.2 1.1 .00 germ .00 .91 .00 2.1 1.3 .01 .00 .76 disk 1.5 .00 1.6 .33 .00 1.3 1.2 .00 body .09 .86 .01 2.0 1.3 .11 .00 .73 pc plasma graphics tissue data ibm cells blood apple

Effect of SVD ,[object Object],[object Object],[object Object],[object Object]

Context Representation ,[object Object],[object Object],[object Object]

Second Order Context Representation ,[object Object],[object Object],[object Object],[object Object],1.0 .72 memory .00 .00 organ .13 1.1 .03 2.7 1.7 .16 .00 .96 linux .00 .91 .00 2.1 1.3 .01 .00 .76 disk Plasma graphics tissue data ibm cells blood apple

Second Order Context Representation ,[object Object]

First vs. Second Order Representations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Analysis ,[object Object],[object Object],[object Object],[object Object]

Sense Discrimination Using Parallel Texts ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Parallel Text ,[object Object],[object Object],[object Object],[object Object],[object Object]

References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Part 7: How to Get Started in Word Sense Disambiguation Research

Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Machine Readable Dictionaries ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Machine Learning Algorithms ,[object Object]

Advances In Wsd Acl 2005

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