Diapositive 1

Overwiew & Objectives

	lexical semantic representations
		conceptual vector model (cvm)
		autonomous learning by the system
		from a given Ç semantic space È (ontology)
	effects of swithing ontologies (general ó spec)
		global effects on the lexicon
		local effects on particular word
		ambiguity as noise
	towards self contained WSD annotations
		Ç I made a deposit at the bank È
		Þ Ç I made a deposit at the bank<g:money> È

Conceptual vectors vector space

	An idea
		Concept combination Ñ a vector
	Idea space
		= vector space
	A concept
		= an idea = a vector V
		with augmentation: V + neighboorhood
	Meaning space
		= vector space + {v}*

2D view of Ç meaning space È

Conceptual vectors Thesaurus

	H : thesaurus hierarchy Ñ K concepts
		Thesaurus Larousse = 873 concepts
	V(Ci) : <a1, É, ai, É , a873>
		aj = 1/ (2 ** Dum(H, i, j))

Conceptual vectors Concept c4:peace

Conceptual vectors Term ÒpeaceÓ

Diapositive 8

Angular distance

	DA(x, y)  = angle (x, y)
		0 £ DA(x, y) £ p
		if 0 then x & y colinear Ñ same idea
		if p/2 then nothing in common
		if p then DA(x, -x) with -x  Ñ anti-idea of x

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) ³ DA(x, z)
			DA(0, 0) = 0  and  DA(x, 0) = p/2 by definition
			DA(ax, by) = DA(x, y) with ab > 0
			DA(ax, by) = p - DA(x, y) with ab < 0
			DA(x+x, x+y)  = DA(x, x+y) £ DA(x, y)

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

Some vector operations

2D view of weak contextualization

Autonomous learning 1/2

Autonomous learning 2/2

Local expansion of vector space

Diapositive 17

Diapositive 18

Lexical Distribution from Local density

Diapositive 20

Diapositive 21

Last words

	Switching of representation
		coarse grained to fine grained Þ better semantic discrimation
		É and vice-versa Þ conservation of resource
	global and local test functions
		for vector quality assessment
		decision taking about level of representation
		detectors when combined to lexical functions (antonymy, etc.)
	the basis for
		self adjustement toward a vector space of constant density
		wsd as a reduction of noise (in context or out of context)
		unification of ontologies
		self emergent structuration of terminology