1. Antonymy and Conceptual Vectors
Didier Schwab, Mathieu Lafourcade, Violaine Prince
presented by Ch. Boitet (works with M. Lafourcade on conceptual vectors & UNL)
Laboratoire d’informatique, de robotique
Et de microélectronique de Montpellier
CNRS - Université Montpellier II

2. Outline The main idea Background on conceptual vectors How we use CVs
& why we need to distinguish CVs of antonyms
Brief study of antonymies
Representation of antonymies
Measure for « antonymousness »
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors
Didier Schwab

3. The main idea
Work on meaning representation in NLP, using conceptual vectors (CV)
applications = WSD & thematic indexing
but V(existence) = V(non-existence) !
basic « concepts » activated the same
Idea:
use lexical functions to improve the adequacy
For this, « transport » the lexical functions in the vector space
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors
Didier Schwab

4. Background on conceptual vectors
Lexical Item = ideas = combination of concepts = Vector V
Ideas space = vector space (generator space)
Concept = idea = vector Vc
Vc taken from a thesaurus hierarchy (Larousse)
translation of Roget’s thesaurus, 873 leaf nodes
the word ‘peace’ has non zero values for concept PEACE and other concepts
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

5. Our conceptual vectors Thesaurus
H : thesaurus hierarchy — K concepts
Thesaurus Larousse = 873 concepts
V(Ci) : <a1, …, ai, … , a873>
aj = 1/ (2 ** Dum(H, i, j))
1/16
1/16
1/4 1
1/4
1/4
1/64
1/64 4 2 6
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors
Didier Schwab 93

6. Conceptual vectors Concept c4: ‘PEACE’
conflict relations
hierarchical relations
The world, manhood
society
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

7. Conceptual vectors Term “peace”
c4:’PEACE’
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

8. exchange profit finance
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

9. Angular or « thematic » distance
Da(x,y) = angle(x,y) = acos(sim(x,y))
= acos(x.y /|x ||y |)
0 ≤ D(x,y) ≤  (positive components)
If 0 then x and y are colinear : same idea.
If /2 : nothing in common. x y
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

10. Thematic Distance (examples)
Da(anteater , anteater ) = 0 (0°)
Da(anteater , animal ) = 0,45 (26°)
Da(anteater , train ) = 1,18 (68°)
Da(anteater , mammal ) = 0,36 (21°)
Da(anteater , quadruped ) = 0,42 (24°)
Da(anteater , ant ) = 0,26 (15°)
thematic distance ≠ ontological distance
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

11. Vector Proximity
Function V gives the vectors closest to a lexical item.
V (life) = life, alive, birth…
V (death) = death, to die, to kill…
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

12. How we build & use conceptual vectors
Conceptual vectors give thematic representations
of word senses
of words (averaging CVs of word senses)
of the content (« ideas ») of any textual segment
New CVs for word senses are permanently learned from NL definitions
coming from electronic dictionaries
CVs of word senses are permanently recomputed
for French, 3 years, words, CVs
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

13. Continuous building of the conceptual vectors database
Definitions
Human usage dictionaries
Conceptual vectors
base
SYGMART
Morphosyntactic analysis
New Vector
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

14. We should distinguish CVs of different but related words…
Non-existent : who or which does not exist
cold : #ant# warm, hot
Without a specific treatment, we get
V(non-existence) = V(existence)
V(cold) = V(hot)
We want to obtain
V(non-existence) ≠ V(existence)
V(cold) ≠ V(hot)
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

15. …in order to improve applications and resources
Applications: more precision
Thematic analysis of texts
Thematic analysis of definitions
Resources: coherence & adequacy
General coherence of the CV data base
Conceptual Vector quality (adequacy)
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

16. Lexical functions may help!
Lexical function (Mel’tchuk):
WS  {WS1…WSn}
synonymy (#Syn#), antonymy (#Anti#), intensification (#Magn#)…
Examples :
#Syn# (car) = {automobile}
#Anti# (respect) = {disrespect; disdain}
#Sing# (fleet) = {boat, ship; embarcation}
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

17. Method: transport the LFs as functions on the CV space
e.g. for antonymy,
to get V(non-existence) ≠ V(existence)
find vector function Anti such that:
V(non-existence)
= V(#Anti#(existence)) = Anti (V(existence))
similarly for other lexical functions
we simply began by studying antinomy
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

18. Brief study of antonymy
Definition :
Two lexical items are in antonymy relation if there is a symmetry between their semantic components relatively to an axis
Antonymy relations depend on the type of medium that supports symmetry
There are several types of antonymy
On the axis, there are fixed points:
Anti (V(car)) = V(car) because #Anti# (car) = 
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

19. 1- Complementary antonymy
Values are boolean & symmetric (01)
Examples :
event/non-event dead/alive
existence/non-existence
He is present  He is not absent
He is absent  He is not present
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

20. 2- Scalar antonymy Values are scalar
Symmetry is relative to a reference value
Examples : cold/hot, small/tall
This man is small  This man is not tall
This man is tall  This man is not small
This man is neither tall nor small
reference value = « of medium height »
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

21. 3- Dual Antonymy (1) Conversive duals
same semantics but inversion of roles
Examples : sell/buy, husband/wife, father/son
Jack is John’s son  John is Jack’s father
Jack sells a car to John  John buys a car from Jack
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

22. 3- Dual Antonymy (2) Cultural : sun/moon, yin/yang
Contrastive duals
contrastive expressions accepted by usage
Cultural : sun/moon, yin/yang
Associative : question/answer
Spatio-temporal : birth/death, start/finish
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

23. Coherence and adequacy of the base
Learning bootstrap based on a kernel composed of pre-computed vectors considered as adequate
Learning must be coherent = preserve adequacy
Adequacy = judgement that activations of concepts (coordinates) make sense for the meaning corresponding to a definition
For coherence improvement, we use semantic relations between terms
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

24. Antonymy function
Based on the antonym vectors of concepts : one list for each kind of antonymy
Antic (EXISTENCE) = V (NON-EXISTENCE)
Antis (HOT) = V (COLD)
Antic (GAME) = V (GAME)
Anti (X,C) builds the vector « opposite » of vector X in context C
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

25. Construction of the antonym vector of X in context C
The method is to focus on the salient notions in V(X) and V(C)
If the notions can be opposed, then the antonym should have the inverse ideas in the same proportions
The following formula was obtained after several experiments
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

26. Construction of the antonym vector (2)
AntiR (V(X), V(C)) =  Pi *AntiC (Ci, V(C))
Pi = V * max (V(X), V(Ci))
Not symmetrical
Stress more on vector X than on context C
Consider an important idea of the vector to oppose even if it is not in the referent
i=1
1+CV(V(X)) Xi
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

27. Results
V (#Anti# (death, life & death)) = (LIFE 0,3), (birth 0,48), (alive 0,54)…
V (#Anti# (life, life & death)) = (death 0,336), (killer 0,45), (murdered 0,53)…
V (#Anti# (LIFE)) = (DEATH 0,034), (death 0,43), (killer 0,53)...
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

28. Antonymy evaluation measure
Assess « how much » two lexical items are antonymous
Manti(A,B) = DA(AB, Anti(A,C) Anti(B,C)) A
Anti(B)
Anti(A) B
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

29. Examples Manti (EXISTENCE, NON-EXISTENCE) = 0,03
Manti (EXISTENCE, CAR) = 1,45
Manti (existence, car) = 1,06
Manti (CAR, CAR) = 0,006
Manti (car, car) = 0,407
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors

30. Conclusion and perspectives
Progress so far :
Antonymy definition based on a notion of symmetry
Implemented formula to compute an antonym vector
Implemented measure to assess the level of antonymy between two items
Perspectives :
Use of the symbolic opposition found in dictionaries
Search the opposite meaning of a word
Study of the other semantic relations
(hyperonymy/hyponymy, meronymy/holonymy…)
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors