Antonymy and Conceptual Vectors Didier Schwab, Mathieu Lafourcade, Violaine Prince Laboratoire...
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Transcript of Antonymy and Conceptual Vectors Didier Schwab, Mathieu Lafourcade, Violaine Prince Laboratoire...
Antonymy and
Conceptual Vectors
Didier Schwab, Mathieu Lafourcade, Violaine Prince
Laboratoire d’informatique, de robotiqueEt de microélectronique de MontpellierCNRS - Université Montpellier II
presented by Ch. Boitet (works with M. Lafourcade on conceptual vectors & UNL)
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors
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
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
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
Our conceptual vectors Thesaurus
• H : thesaurus hierarchy — K conceptsThesaurus Larousse = 873 concepts
• V(Ci) : <a1, …, ai, … , a873>aj = 1/ (2 ** Dum(H, i, j))
1/41 1/41/41/161/16 1/64 1/64
2 64
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors
Conceptual vectors Concept c4: ‘PEACE’
peace
hierarchical relations
conflict relations
The world, manhood society
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors
Conceptual vectors Term “peace”
c4:’PEACE’
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors
finance
profitexchange
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors
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
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
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
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, 100000 words, 300000 CVs
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors
SYGMART Morphosyntactic analysis
DefinitionsHuman usage dictionaries
Conceptual vectorsbase
New Vector
Continuous building of the conceptual vectors
database
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors
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 obtainV(non-existence) ≠ V(existence) V(cold) ≠ V(hot)
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors
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)
…in order to improve applications and resources
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors
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
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 functionswe simply began by studying antinomy
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors
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
1- Complementary antonymy
Values are boolean & symmetric (01)
Examples : event/non-event dead/alive
existence/non-existence
He is present He is not absentHe is absent He is not present
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors
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 tallThis man is tall This man is not small
This man is neither tall nor smallreference value = « of medium height »
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors
3- Dual Antonymy (1)
Conversive dualssame 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
3- Dual Antonymy (2) 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
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
Coherence and adequacy of the base
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors
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
Antonymy function
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors
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
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
Construction of the antonym vector (2)
i=1N
Xi
1+CV(V(X))
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors
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
Antonymy evaluation measure
Assess « how much » two lexical items are antonymous
Manti(A,B) = DA(AB, Anti(A,C) Anti(B,C))
A
B
Anti(B)
Anti(A)
Schwab, Lafourcade, Prince, pres. by Ch. Boitet
Antonymy and Conceptual Vectors
Examples
Manti (EXISTENCE, NON-EXISTENCE) = 0,03
Manti (existence, non-existence) = 0,44 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
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…)