Languages are inherently ambiguous. Four out of five words in English have more than one meaning. Nowadays there is a growing number of small proprietary thesauri used for knowledge management for different applications. In order to enable the usage of these thesauri for automatic text annotations, we introduce a robust method for dis- criminating word senses using hypernyms. The method uses collocations to induce word senses and to discriminate the thesaural sense from the other senses by utilizing hypernym entries taken from a thesaurus. The main novelty of this work is the usage of hypernyms already at the stage sense induction. The hypernyms enable us to cast the task to a binary scenario, namely teasing apart thesaural senses from all the rest. The introduced method outperforms the baseline and has indicates accuracy above 80%.

1. Discrimination of Word Senses with Hypernyms
Artem Revenko Victor Mireles
research@semantic-web.com
LD4IE @ ISWC
October 22, 2017

2. Problem Statement Method Experimental Results
Problem Statement
Method
Experimental Results
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3. Problem Statement Method Experimental Results
Finding Mentioned Entity
A Knowledge Base
Thing
Animal
Jaguar
Car
BMW
Jaguar
BMW has designed a
car that is going to drive
Jaguar X1 out of the
market.
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4. Problem Statement Method Experimental Results
Finding Mentioned Entity
A Knowledge Base
Thing
Animal
Jaguar
Car
BMW
Jaguar
BMW has designed a
car that is going to drive
Jaguar X1 out of the
market.
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5. Problem Statement Method Experimental Results
Finding Mentioned Entity
A Knowledge Base
Thing
Animal
Jaguar
BMW has designed a car
that is going to drive
Jaguar X1 out of the
market.
?
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6. Problem Statement Method Experimental Results
PoolParty poolparty.biz
Entity Matching for Enabling Search
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7. Problem Statement Method Experimental Results
Problem Statement
Input:
Thesaurus
A
B
C
D
Target Entity
C with
different labels li
Corpus
 l1
 l2
. . .
 lN
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8. Problem Statement Method Experimental Results
Problem Statement
Input:
Thesaurus
A
B
C
D
Target Entity
C with
different labels li
Corpus
 l1
 l2
. . .
 lN
Output:
“This” Corpus
 C
 C
. . .
“Other” Corpus
 C
 C
. . .
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9. Problem Statement Method Experimental Results
Problem Statement
Method
Experimental Results
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10. Problem Statement Method Experimental Results
Overall Description
Plan
1. Find all senses of the target E
Describing Senses
You shall know a word by the company it keeps.
Firth, J. R. 1957
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11. Problem Statement Method Experimental Results
Overall Description
Plan
1. Find all senses of the target E
1 Represent senses space
Describing Senses
You shall know a word by the company it keeps.
Firth, J. R. 1957
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12. Problem Statement Method Experimental Results
Overall Description
Plan
1. Find all senses of the target E
1 Represent senses space
2 Introduce information from the knowledge base
Describing Senses
You shall know a word by the company it keeps.
Firth, J. R. 1957
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13. Problem Statement Method Experimental Results
Overall Description
Plan
1. Find all senses of the target E
1 Represent senses space
2 Introduce information from the knowledge base
3 Cluster senses
Describing Senses
You shall know a word by the company it keeps.
Firth, J. R. 1957
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14. Problem Statement Method Experimental Results
Overall Description
Plan
1. Find all senses of the target E
1 Represent senses space
2 Introduce information from the knowledge base
3 Cluster senses
2. Disambiguate the senses
Describing Senses
You shall know a word by the company it keeps.
Firth, J. R. 1957
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15. Problem Statement Method Experimental Results
Word Sense Induction
 Entity
Term1Term2
Term3
Term4
0.2
0.15
0.12 0.1
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16. Problem Statement Method Experimental Results
Word Sense Induction
 Entity
Term1Term2
Term3
Term4
Term5
Term6
0.2
0.15
0.12 0.1
0.22
0.16
0.2
0.1
0.2
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17. Problem Statement Method Experimental Results
Word Sense Induction
Entity
Term1Term2
Term3
Term4
Term5
Term6
0.2
0.15
0.12 0.1
0.22
0.16
0.2
0.1
0.2
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18. Problem Statement Method Experimental Results
Word Sense Induction
Entity
Term1Term2
Term3
Term4
Term5
Term6
0.2
0.15
0.12 0.1
0.22
0.16
0.2
0.1
0.2
Sense1
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19. Problem Statement Method Experimental Results
Word Sense Induction
Entity
Term1Term2
Term3
Term4
Term5
Term6
0.2
0.15
0.12 0.1
0.22
0.16
0.2
0.1
0.2
Sense1
Sense2
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20. Problem Statement Method Experimental Results
Word Sense Induction
Entity
Term1Term2
Term3
Term4
Term5
Term6
Hypernym
0.2
0.15
0.12 0.1
0.22
0.16
0.2
0.1
0.2
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21. Problem Statement Method Experimental Results
Word Sense Induction
Entity
Term1Term2
Term3
Term4
Term5
Term6
Hypernym
0.2
0.15
0.12 0.1
0.22
0.16
0.2
0.1
0.2
“This” Sense
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22. Problem Statement Method Experimental Results
Word Sense Induction
Entity
Term1Term2
Term3
Term4
Term5
Term6
Hypernym
0.2
0.15
0.12 0.1
0.22
0.16
0.2
0.1
0.2
“This” Sense
“Other” Sense
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23. Problem Statement Method Experimental Results
Example
Americano
45 texts: 13 about cocktail, 32 about coffee.
Potential hubs:
1. “campari”
2. “mix”
3. “espresso”
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24. Problem Statement Method Experimental Results
Example
Americano
45 texts: 13 about cocktail, 32 about coffee.
Potential hubs:
1. “campari”
2. “mix”
3. “espresso”
With hypernyms cocktail, Mixed drink, The Unforgettables: mix .
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25. Problem Statement Method Experimental Results
Example
Americano
45 texts: 13 about cocktail, 32 about coffee.
Potential hubs:
1. “campari”
2. “mix”
3. “espresso”
With hypernyms cocktail, Mixed drink, The Unforgettables: mix .
Reorder potential hubs:
1. “espresso”
2. “campari”
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26. Problem Statement Method Experimental Results
Example
Americano
45 texts: 13 about cocktail, 32 about coffee.
Potential hubs:
1. “campari”
2. “mix”
3. “espresso”
With hypernyms cocktail, Mixed drink, The Unforgettables: mix .
Reorder potential hubs:
1. “espresso”
2. “campari”
Final hubs: mix , espresso .
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27. Problem Statement Method Experimental Results
Problem Statement
Method
Experimental Results
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28. Problem Statement Method Experimental Results
Setup
Data: github.com/artreven/thesaural_wsi
Wikilinks http://www.iesl.cs.umass.edu/data/wiki-links
Document URL surface form (label) wiki/concept
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29. Problem Statement Method Experimental Results
Setup
Data: github.com/artreven/thesaural_wsi
Wikilinks http://www.iesl.cs.umass.edu/data/wiki-links
Document URL surface form (label) wiki/concept
Cocktails
Knowledge base 250 concepts about cocktails
Corpora 13 corpora, 1227 texts.
Examples Americano, B52, Margarita, . . .
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30. Problem Statement Method Experimental Results
Setup
Data: github.com/artreven/thesaural_wsi
Wikilinks http://www.iesl.cs.umass.edu/data/wiki-links
Document URL surface form (label) wiki/concept
Cocktails
Knowledge base 250 concepts about cocktails
Corpora 13 corpora, 1227 texts.
Examples Americano, B52, Margarita, . . .
MeSH
Knowledge base 28000 concepts about cocktails
Corpora 8 corpora, 784 texts.
Examples Amnesia, Vertigo, . . .
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31. Problem Statement Method Experimental Results
Evaluation
Cocktails
Method Macro average Micro average
Baseline 0.725 0.737
HyperLex 0.784 0.821
HyperHyperLex 0.841 0.896
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32. Problem Statement Method Experimental Results
Evaluation
Cocktails
Method Macro average Micro average
Baseline 0.725 0.737
HyperLex 0.784 0.821
HyperHyperLex 0.841 0.896
MeSH
Method Macro average Micro average
Baseline 0.68 0.735
HyperLex 0.617 0.621
HyperHyperLex 0.723 0.739
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33. Problem Statement Method Experimental Results
Conclusion
Link to domain specific knowledge base without having
knowledge of all senses!
Combined unsupervised / knowledge-based approach
Takes knowledge graph into account, but no external resources
Thank you!Thank you!
research.semantic-web.at
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34. Problem Statement Method Experimental Results
Conclusion
Link to domain specific knowledge base without having
knowledge of all senses!
Combined unsupervised / knowledge-based approach
Takes knowledge graph into account, but no external resources
Thank you!Thank you!
research.semantic-web.at
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