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Published byAnne-Laure Bordeleau Modified over 5 years ago
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Automatically Populating Acception Lexical Database through Bilingual Dictionaries and Conceptual Vectors PAPILLON 2002 Mathieu Lafourcade LIRMM - France 1
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Overwiew & Objectives Lexical soup
what ? Bilingual dic & Conceptual vectors which heuristics ? for what ? linking decision and quality assessment
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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
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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
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Conceptual vectors Concept c4:peace
conflict relations hiérarchical relations The world, manhood society
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Conceptual vectors Term “peace”
c4:peace
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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
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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
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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
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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
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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
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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
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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 {…}
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… 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 = {…} …
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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
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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
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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 107
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