Download presentation

Presentation is loading. Please wait.

Published byMiguel Fox Modified over 4 years ago

1
S é mantique lexicale Vecteur conceptuels et TALN Mathieu Lafourcade LIRMM - France www.lirmm.fr/~lafourca

2
Analyse sémantique Word Sense Disambiguation Text Indexing in IR Lexical Transfer in MT Conceptual vector Reminiscence Vector Models (Salton) Conceptual Models (Sowa) Concepts (et non des termes) Ensemble E choisi a priori (petit E) / par emergence (grand) Concepts interdépendants Propagation sur arbre danalyse morpho-syntaxique (pas analyse de surface) Objectifs

3
Conceptual vectors An idea = a combination of concepts = a vector The Idea space = vector space A concept = an idea = a vector = combination of itself + neighborhood

4
Conceptual vectors Set of k basic concepts Thesaurus Larousse = 873 concepts A vector = a 873 uple Encoding for each dimension C = 2 15 Sense space = vector space + vector set

5
Conceptual vectors Example : cat Kernel c:mammal, c:stroke Augmented c:mammal, c:stroke, c:zoology, c:love … + Iteration for neighborhood augmentation Finer vectors

6
Vector space Basic concepts are not independent Sense space = Generator Space of a real k vector space (unknown) = Dim k <= k Relative position of points

7
Our conceptual vectors Thesaurus H : thesaurus hierarchy K concepts Thesaurus Larousse = 873 concepts V(C i ) : a j = 1/ (2 ** D um (H, i, j)) 1/41 1/16 1/64 264

8
Conceptual vectors Concept c4: PEACE peace hierarchical relations conflict relations The world, manhood society

9
Conceptual vectors Term peace c4:PEACE

10
finance profit exchange

11
Conceptual vector distance Angular Distance D A (x, y) = angle (x, y) 0 <= D A (x, y) <= π if 0 then collinear - same idea if π /2 then nothing in common if π then D A (x, -x) with -x as anti-idea of x x y x

12
Conceptual vector distance Distance = acos(similarity) D A (x, y) = acos( ((x 1 y 1 + … + x n y n )/|x||y|)) D A (x, x) = 0 D A (x, y) = D A (y, x) D A (x, y) + D A (y, z) D A (x, z) D A (0, 0) = 0 and D A (x, 0) = π /2 by definition D A ( x, y) = D A (x, y) with 0 D A ( x, y) = π - D A (x, y) with 0 D A (x+x, x+y) = D A (x, x+y) D A (x, y)

13
Conceptual vector distance Example D A (tit, tit) = 0 D A (tit, passerine) = 0.4 D A (tit, bird) = 0.7 D A (tit, train) = 1.14 D A (tit, insect) = 0.62 tit = kind of insectivorous passerine …

14
Conceptual lexicon Set of (word, vector) = (w, )* Monosemy word 1 meaning 1 vector (w, ) tit

15
Conceptual lexicon Polyseme building Polysemy word n meanings n vectors {(w, ), (w.1, 1 ) … (w.n, n ) } bank

16
Squash isolated meanings against numerous close meanings Conceptual lexicon Polyseme building (w) = (w.i) =.i bank = 1. Mound, 3. river border, … 2. Money institution 3. Organ keyboard 4. … bank

17
Conceptual lexicon Polyseme building (w) = classification(w.i ) bank 1:D A (3,4) & (3+2) 2: (bank 4 ) 7: (bank 2 )6: (bank 1 ) 4: (bank 3 ) 5: D A (6,7)& (6+7) 3: D A (4,5) & (4+5)

18
Lexical scope LS(w) = LS t ( (w)) LS t ( (w)) = 1 if is a leaf LS t ( (w)) = (LS( 1 ) + LS( 2 )) /(2-sin(D( (w))) otherwise (w) = t ( (w)) t ( (w)) = (w) if is a leaf t ( (w)) = LS( 1 ) t ( 1 ) + LS( 2 ) t ( 2 ) otherwise 1:D(3,4), (3+2) 2: 4 7: 2 6: 1 4: 3 5:D(6,7), (6+7) 3:D(4,5), (4+5) Can handle duplicated definitions (w) =

19
Vector Statistics Norm( ) [0, 1] * C (2 15 =32768) Intensity( ) Norm / C Usually = 1 Standard deviation (SD) SD 2 = variance variance = 1/n * (x i - ) 2 with as the arith. mean

20
Vector Statistics Variation coefficient (CV) CV = SD / mean No unity - Norm independent Pseudo Conceptual strength If A Hyperonym B CV(A) > CV(B) (we don t have ) vector « fruit juice » (N) MEAN = 527, SD = 973 CV = 1.88 vector « drink » (N) MEAN = 443, SD = 1014 CV = 2.28

21
Vector operations Sum V = X + Y v i = x i + y i Neutral element : 0 Generalized to n terms : V = V i Normalization of sum : v i /|V|* c Kind of mean

22
Vector operations Term to term product V = X Y v i = x i * y i Neutral element : 1 Generalized to n terms V = V i Kind of intersection

23
Vector operations Amplification V = X ^ n v i = sg(v i ) * |v i |^ n V = V ^ 1/2and n V = V ^ 1/n V V = V ^ 2if v i 0 Normalization of ttm product to n terms V = n V i

24
Vector operations Product + sum V = X Y = ( X Y ) + X + Y Generalized n terms : V = n V i + V i Simplest request vector computation in IR

25
Vector operations Subtraction V = X Y v i = x i y i Dot subtraction V = X Y v i = max (x i y i, 0) Complementary V = C(X) v i = (1 x i c) * c etc. Set operations

26
Intensity Distance Intensity of normalized ttm product 0 ( (X Y)) 1 if |x| = |y| = 1 D I (X, Y) = acos( ( X Y)) D I (X, X) = 0and D I (X, 0) = π/2 D I (tit, tit) = 0(D A = 0) D I (tit, passerine) = 0.25(D A = 0.4) D I (tit, bird) = 0.58(D A = 0.7) D I (tit, train) = 0.89 (D A = 1.14) D I (tit, insect) = 0.50(D A = 0.62)

27
Relative synonymy Syn R (A, B, C) C as reference feature Syn R (A, B, C) = D A (A C, B C) D A (coal,night) = 0.9 Syn R (coal, night, color) = 0.4 Syn R (coal, night, black) = 0.35

28
Relative synonymy Syn R (A, B, C) = Syn R (B, A, C) Syn R (A, A, C) = D (A C, A C) = 0 Syn R (A, B, 0) = D (0, 0) = 0 Syn R (A, 0, C) = π /2 Syn A (A, B) = Syn R (A, B, 1) = D (A 1, B 1) = D (A, B)

29
Subjective synonymy Syn S (A, B, C) C as point of view Syn S (A, B, C) = D(C-A, C-A) 0 Syn S (A, B, C) π normalization: 0 asin(sin(Syn S (A, B, C))) π/2 B A C C C

30
Subjective synonymy When |C| then Syn S (A, B, C) 0 Syn S (A, B, 0) = D(-B, -A) = D(A, B) Syn S (A, A, C) = D(C-A, C-A) = 0 Syn S (A, B, B) = Syn S (A, B, A) = 0 Syn S (tit, swallow, animal) = 0.3 Syn S (tit, swallow, bird) = 0.4 Syn S (tit, swallow, passerine) = 1

31
68 Semantic analysis Vectors propagate on syntactic tree Lesrapidement P GV GVA GNP termites attaquent lesfermes GN dutoit The white ants strike rapidly the trusses of the roof

32
Semantic analysis Lesrapidement P GV GVA GNP attaquent fermes termites les GN toit GN du farm business farm building truss roof anatomy above to start to attack to Critisize

33
Semantic analysis Initialization - attach vectors to nodes Lesrapidement P GV GVA GNP termites attaquent lesfermes GN dutoit 1 2 3 4 5

34
Semantic analysis Propagation (up) Lesrapidement P GV GVA GNP termites attaquent lesfermes GN dutoit

35
Semantic analysis Back propagation (down) (N i j ) = ( (N i j ) (N i )) + (N i j ) Lesrapidement P GV GVA GNP termites attaquent lesfermes GN dutoit 1 2 3 4 5

36
Semantic analysis Sense selection or sorting Lesrapidement P GV GVA GNP termites attaquent les fermes GN du toit farm business farm building truss roof anatomy above to start to attack to Critisize

37
Sense selection 1:D(3,4) & (3+2) 2: (bank 4 ) 7: (bank 2 )6: (bank 1 ) 4: (bank 3 ) 5:D(6,7)& (6+7) 3:D(4,5) & (4+5) Recursive descent on t(w) as decision tree D A (, i ) Stop on a leaf Stop on an internal node

38
Vector syntactic schemas S: NP(ART,N) (NP) = V(N) S: NP1(NP2,N) (NP1) = (NP1)+ (N)0< <1 (sail boat) = (sail) + 1/2 (boat) (boat sail) = 1/2 (boat) + (sail)

39
Vector syntactic schemas Not necessary linear S: GA(GADV(ADV),ADJ) (GA) = (ADJ)^p(ADV) p(very) = 2 p(mildly) = 1/2 (very happy) = (happy)^2 (mildly happy) = (happy)^1/2

40
Iteration & convergence Iteration with convergence Local D( i, i+1 ) for top Global D( i, i+1 ) for all Good results but costly

41
Lexicon construction Manual kernel Automatic definition analysis Global infinite loop = learning Manual adjustments iterations synonyms Ukn word in definitions kernel

42
Application machine translation Lexical transfer source target Knn search that minimizes D A ( source, target ) Submeaning selection Direct Transformation matrix chat cat

43
Application Information Retrieval on Texts Textual document indexation Language dependant Retrieval Language independent - Multilingual Domain representation horse equitation Granularity Document, paragraphs, etc.

44
Application Information Retrieval on Texts Index = Lexicon = (d i, i )* Knn search that minimizes D A ( (r), (d i )) doc 1 doc n doc 2 request

45
Search engine Distances adjustments Min D A ( (r), (d i )) may pose problems Especially with small documents Correlation between CV & conceptual richness Pathological cases « plane » and « plane plane plane plane … » « inundation » « blood » D = 0.85 (liquid)

46
Search engine Distances adjustments Correction with relative intensity Request vs retrieved doc ( r and d ) D = (D A ( r, d ) * D I ( r, d )) 0 ( r, d ) 1 0 D I ( r, d ) π/2

47
Conclusion Approach statistical (but not probabilistic) thema (and rhema ?) Combination of Symbolic methods (IA) Transformational systems Similarity Neural nets With large Dim (> 50000 ?)

48
Conclusion Self evaluation Vector quality Tests against corpora Unknown words Proper nouns of person, products, etc. Lionel Jospin, Danone, Air France Automatic learning Badly handled phenomena? Negation & Lexical functions (Meltchuk)

49
End 1. extremity 2. death 3. aim …

Similar presentations

Presentation is loading. Please wait....

OK

How to analyse your related texts and prescribed text

How to analyse your related texts and prescribed text

© 2018 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google