1. Automatically Populating Acception Lexical Database through Bilingual Dictionaries and Conceptual Vectors
PAPILLON 2002
Mathieu Lafourcade
LIRMM - France 1

2. Overwiew & Objectives Lexical soup
what ? Bilingual dic & Conceptual vectors
which heuristics ?
for what ? linking decision and quality assessment

3. Conceptual vectors vector space
An idea
Concept combination — a vector
Idea space
= vector space
A concept
= an idea = a vector V
with augmentation: V + neighboorhood
Meaning space
= vector space + {v}*  27

4. 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 93

5. Conceptual vectors Concept c4:peace
conflict relations
hiérarchical relations
The world, manhood
society

6. Conceptual vectors Term “peace”
c4:peace

7. Angular distance DA(x, y) = angle (x, y) 0  DA(x, y)  
if 0 then x & y colinear — same idea
if /2 then nothing in common
if  then DA(x, -x) with -x — anti-idea of x x’ x  y 36

8. Angular distance DA(x, y) = acos(sim(x,y))
DA(x, y) = acos(x.y/|x||y|))
DA(x, x) = 0
DA(x, y) = DA(y, x)
DA(x, y) + DA(y, z)  DA(x, z)
DA(0, 0) = 0 and DA(x, 0) = /2 by definition
DA(x, y) = DA(x, y) with   0
DA(x, y) =  - DA(x, y) with  < 0
DA(x+x, x+y) = DA(x, x+y)  DA(x, y) 37

9. Thematic distance Examples DA(tit, tit) = 0 DA(tit, passerine) = 0.4
DA(tit, bird) = 0.7
DA(tit, train) = 1.14
DA(tit, insect) = 0.62
tit = insectivorous passerine bird … 43

10. Conceptual vector base
English entry base
French entry base   
fleuve
river   
rivière
acc y
map
acc
carte.1
carte.2
card.1
acc x
Malay entry base
Japanese entry base
acc z
sungai   
acc x               
Acception base

11. Conceptual vector base
French entry base
fleuve v
fleuve v
rivière v
rivière v
acc x
carte.2 v
carte.2 v
carte.1 v
carte.1 v
acc y
acc x
empty
acc z         
Acception base

12. English Vectorized monolingual dictionary English-French
Bilingual dictionary
Left over meaning
demand
Association v
demand.1
glosses
equivalents
demand v
demand.2
glosses
equivalents
demand.1 v
def v
demand.3
glosses
equivalents
demand.2 v
def v
demand.4
glosses
equivalents
demand.3 v
def v
demand.5
glosses
equivalents
demand.4 v
def
Vector space
demand.1 v
def
Equivalents 2
Glosses 2
demand.1 v
def
Equivalents 2
Glosses 2
demand.1 v
def
Equivalents 2
Glosses 2
demand.1 v
def
Equivalents 2
Glosses 2
Associations between definitions, vectors, glosses and equivalents

13. Source Language Target Language Acceptions already existing link
created link
W-SL i
equiv = {W-TL}
W-TL
equiv = {W-SL, …}   
warning if not close
vectors are close v
W-SL i
{W-TL} v
W-TL
{W-SL, …}    v
W-TL
{W-SL, …}    v
W-TL
{W-SL, …}   
left over acceptions
W-TL
{…}   

14. … Source Language Acceptions Target Language v W-TL1 Equiv = {W-SL, …}   v
W-TL1
Equiv = {W-SL, …}    v
W-TL1
Equiv = {W-SL, …}   
W-TL1
Equiv = {…} v
W-SL i
{W-TL1,W-TL2, …}    v
W-TL2
Equiv = {W-SL, …}    v
W-TL2
Equiv = {W-SL, …}    v
W-TL2
Equiv = {W-SL, …}   
W-TL2
Equiv = {…}    …

15. created acception refinement links closest vector W-SL i equiv = W-TL  
W-SL i
equiv = W-TL   
created acception   
W-SL i
equiv = W-TL   
refinement
links   
W-SL i
equiv = W-TL   
W-SL i
equiv = W-TL
closest vector
W-SL i
equiv = W-TL   
W-SL i
equiv = W-TL

16. 1 2 closest vector W-SL i equiv = W-TL W-SL j equiv = W-TL W-SL j  
W-SL j
equiv = W-TL   
W-SL j
equiv = W-TL 1
W-SL i
equiv = W-TL      
W-SL j
equiv = W-TL   
W-SL j
equiv = W-TL 2
W-SL i
equiv = W-TL      
W-SL j
equiv = W-TL   
W-SL j
equiv = W-TL
closest vector

17. Conclusion System in continuous learning
Evolving results
Hopefully converging
Assisting and begin assisted by
Vectorized lexical functions
Human annotators
Toward
Community of lexical agents
Lexical knowledge negotiation
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