Word sense disambiguation Study on word net ontology

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Word sense disambiguationStudy on word net
ontology

• Akilan Velmurugan
• Computer Networks CS 790G

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Overview

• What is WSD ?
• How wordnet is analyzed as a Complex Network
• What are the results
• Project Methodology
• Area of study
• Key Findings/Results
• New approaches
• Improvement techniques
• Conclusion

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Project Description

• Objective
• Study on WSD
• Effects of WSD in Word Sense Ontology
• Characteristics of WordNet
• Results
• How do match words with other words
• Parameters taken for study of word sense
• Improvise them by making necessary changes
• Study network characteristics

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WordNet - overview

• Machine readable semantic dictionary interlinked
  by semantic relations
• Developed at Princeton University as a large
  lexical database for English language
• Most widely used linguistic resource
• Free for public (GPL )
• Forms a scale free network with small average
  shortest path having words as nodes and concepts
  as links
• Easily navigable

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WordNet (Structure)

• Shows the relation in the form of
• Noun, Verb, Adjective, adverb
• Synonym
• Hypernym (Is a kind of )
• Hyponym ( Is a kind of)
• Troponym (particular ways to )
• Meronym (parts of )
• ---- about 25 relations
• Also available for online navigation

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WordNet online - by Princeton University
WordNet online
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WordNet Browser
Wordnet Application
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WordNet (working)

• WSD
• Corpus based approaches
• Set of samples that enables the system
• Knowledge based approaches
• Machine readable dictionary with relations
• WordNet Research
• Open source
• Ranking of synsets derived from word frequencies
  in the British National Corpus
• Top 1000
• Content manipulation of text
• Dataset I controlled and calibrated study
• Dataset II collected using mechanical trunk
  using pairs

WordNet Database
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Word Sense Disambiguation (WSD)

• Task of determining the meaning of an ambiguous
  word in the given context
• Bank
• Edge of a river
• or
• Financial institution that accepts money
• Refers to the resolution of lexical semantic
  ambiguity and its goal is to attribute the
  correct senses to words (AI-complete problem)

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WSD Area of Research

• Assigning correct sense to words having
  electronic dictionary as source of word
  definitions
• Open research field in Natural Language
  Processing (NLP)
• Hard Problem which is a popular area for research
• Used in speech synthesis by identifying the
  correct sense of the word

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JavaScript Visual WordNet
Visual WordNet
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Visual Thesaurus
Visual Thesaurus
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WordNet Theoretical aspects

• Wordnet word sense ontology
• Symbols are words
• Synset list of words and semantic relations
  between them
• Word sense disambiguation
• Wordnet structure using latent semantics
• Variable lexical notation for a concept
• Citibase Thesaurus
• Semantic relatedness
• And few others

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WSD using latent semantics

• Measures the semantic distance of concepts
• Relatedness and between-ness are calculated
• Matrix form of wordnet data structure is used
• Can be used to integrate with other applications
• Uses Singular Value Decomposition (SVD) algorithm
• Example Multiple synsets are
• car, gondola
• car, railway car
• car, automobile
• Motor vehicle, Coupe, Sedan, Taxi

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MDS-example
1, 2, 3, 4, 10, 12
5, 6, 7, 8, 9, 11, 13
k-means
S
Geodesic Distance Matrix
-1.22 -0.12
-0.88 -0.39
-2.12 -0.29
-1.01 1.07
0.43 -0.28
0.78 0.04
1.81 0.02
-0.09 -0.77
-0.09 -0.77
0.30 1.18
2.85 0.00
-0.47 2.13
-0.29 -1.81
1 2 3 4 5 6 7 8 9 10 11 12 13
1 0 1 1 1 2 2 3 1 1 2 4 2 2
2 1 0 2 2 1 2 3 2 2 3 4 3 3
3 1 2 0 2 3 3 4 2 2 3 5 3 3
4 1 2 2 0 3 2 3 2 2 1 4 1 3
5 2 1 3 3 0 1 2 2 2 2 3 3 3
6 2 2 3 2 1 0 1 1 1 1 2 2 2
7 3 3 4 3 2 1 0 2 2 2 1 3 3
8 1 2 2 2 2 1 2 0 2 2 3 3 1
9 1 2 2 2 2 1 2 2 0 2 3 3 1
10 2 3 3 1 2 1 2 2 2 0 3 1 3
11 4 4 5 4 3 2 1 3 3 3 0 4 4
12 2 3 3 1 3 2 3 3 3 1 4 0 4
13 2 3 3 3 3 2 3 1 1 3 4 4 0
MDS
Source Lecture18 Community Structure by
Prof.Gunes
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WSD using latent semantics
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WSD variable lexical notations for a concept

• Generic concept notation
• D I ? J ? K
• ? J D - (I ? K)
• (D - I )n(D - K)
• Dn (I? K)
• J Dn ( I nK)
• since, B D ? E ? F
• D B - (E?F)
• (B - E)n(B - F)
• Bn(E ?F)
• D B n(E n F)

Source Proceedings of the 20th International
Conference on Advanced Information Networking and
Applications
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WSD variable lexical notations for a concept

• J Dn ( I nK)
• ( Bn(E n F) )n( I n K)
• J Bn( (E n F)n( I n K) )
• when J fly,
• D fish lure
• I spinner
• k troll
• And introducing boolean operators,
• AND for n
• OR for ?
• NOT for

Source Proceedings of the 20th International
Conference on Advanced Information Networking and
Applications
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WSD variable lexical notations for a concept

• (fly) becomes
• (fisherman's lure OR fish lure) AND (
  (NOT spinner) AND (NOT troll) )
• then B lure,
• E ground bait,
• F stool pigeon
• (fly) becomes
• (bait OR decoy OR lure) AND ( ((NOT
  ground bait) AND (NOT stoolpigeon) AND((NOT
  spinner)AND(NOT troll)) )

Source Proceedings of the 20th International
Conference on Advanced Information Networking and
Applications
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Thesaurus as a complex network

• As a Directed Graph
• sink composed of the 73,046 terms with kout 0
• source are the 30,260 terms with at least one
  outgoing link (kout gt 0) Root words
• absolute source without incoming links kin 0
• normal source (kout gt 0 and kin gt 0)
• bridge source without outgoing links to root
  words (kout(source) 0)

1 Normal source 2 Bridge source 3 Absolute
source 4 sink
Source arXivcond-mat/0312586 v1 2003
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WSD Semantic relatedness and word sense
disambiguation

• Concepts that occur more frequently and closer
  with each others are more related to each
  others than the concepts that appear less
  frequently and farther one

Source Proceedings of the 20th International
Conference on Advanced Information Networking and
Applications
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WordNet Relationship

• Semantic relatedness
• Involves relationships among words
• car-wheel (meronym)
• hot-cold (antonym)
• pencil-paper (functional)
• penguin-antarctica (association)
• Bank-trust company (synonym)
• Probability and Distance calculation
• Frequency of synsets or words
• Performance in NLP applications

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WordNet Relationship Browser
WordNet Relationship Browser
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WordNet Connect

• Program to find all possible connections between
  two words in WordNet
• Used in computing Semantic Opposition among word
  sense ontology
• WordNet lexical database dictionary is used to
  read the semantic relations
• Capabilities like number of paths, shortest path,
  overall network structure is studied

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WordNet Connect
WordNet Connect
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WordNet Connect
WordNet Connect
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WordNet Connect
WordNet Connect
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Future work

• WordNet structure in terms of complex network
• Key assumptions
• WordNet lexical dictionary analyzed under the
  scope of source node, target node with an
  additional reference node
• Achieve a cost effective path which is
  conditionally related to mean reference node
• Control the path traversal with a relation of
  focus
• Include Common File Number to make it more
  efficient

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Conclusion

• A single visualization can not reveal the entire
  structure of wordnet
• There are different ways of analyzing the
  effectiveness of the overall system
• A new method to evaluate the usefullness of the
  WordNet network structure

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Questions and Comments