Optimistic vs. Pessimistic Interpretation of Linguistic Negation

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1 Optimistic vs. Pessimistic Interpretation of Linguistic Negation Daniel Pacholczyk 1, Mohamed Quafafou 2, and Laurent Garcia 1 1 LERIA, University of Angers, 2 Bd Lavoisier, 49045, ANGERS Cedex 01, FRANCE {pacho,garcia}@info.univ-angers.fr 2 IRIN, University of Nantes, 2 Rue de la Houssiniere, BP 92208, 44322, NANTES Cedex 3, FRANCE Mohamed.Quafafou@irin.univ-nantes.fr Abstract. Linguistic negation processing is a challenging problem studied by a large number of researchers from different communities, i.e. logic, linguistics, etc. We are interested in finding the positive interpretations of a negative sentence represented as x is not A. In this paper, we do not focus on the single set of translations but on two approximation sets. The first one called pessimistic corresponds to the positive translations of the negative sentence that we can consider as sure. The second one called optimistic contains all the sentences that can be viewed as possible translations of the negative sentence. These approximation sets are computed according to the rough sets framework and based on a neighbourhood relation defined on the space of properties. Finally, we apply an original strategy of choice upon the two approximation sets which allows us to select the suitable translations of the initial negative sentence. It appears that we obtain results in good accordance with the ones linguistically expected. 1 Introduction When dealing with sentences expressed in natural language, it is difficult to define precisely their meaning since they are imprecise, ambiguous, etc. However, a human reasoning with such information is able to find a specific interpretation of these sentences. The methods used to manage these information are based on deep natural language analysis, the notion of context, etc ([6], [2], [8], [7], [21]). Here, the framework we are interested in is the one dealing with information, expressed in natural language, using a negation like John is not tall or John is not really small ([9], [1], [5], [4]). The issue is then to find a positive interpretation of a such negative sentence. Let us notice that different significances may be associated with this sentence, like John is extremely tall or John is very small ([10], [12], [11], [13]). The goal of this paper is made of three parts: (1) analyze the problem of linguistic negation, (2) introduce a formalization of linguistic negation approximation using rough set theory, (3) propose a formal framework for selecting automatically the certain and possible interpretations of a linguistic negation. This paper is organized as follows. Section 2 is dedicated to the introduction of the main concepts and notations related to the problem of linguistic negation which is viewed here as a negation of a nuanced property. Section 3 is devoted to the definition of the reference frame from which one can extract (if needed) the affirmative, called also positive, interpretations of a linguistic negation. This approach takes into account some D. Scott (Ed.): AIMSA 2002, LNAI 2443, pp , c Springer-Verlag Berlin Heidelberg 2002

2 Optimistic vs. Pessimistic Interpretation of Linguistic Negation 133 results of linguistic analysis of linguistic negation proposed in ([9], [1], [4], [5]). This new definition of reference frame can be viewed as a generalization of the one proposed in [13]. Section 4 presents a new approach to linguistic negation: the originality results from the fact that we do not search directly all affirmative interpretations of a negative sentence, but we approximate its significance. Our approach is based on two optimistic and pessimistic operators that are defined according to rough set theory ([14], [15], [16]). These operators refer to a specific neighbourhood (or similarity) relation defined in such a way that the pessimistic operator returns certain positive interpretations whereas the result of the optimistic operator contains all possible positive interpretations. We briefly recall the basic notions of rough set theory in Section 4.1. The linguistic negation reformulation is presented in Section 4.2. Its approximation within rough set theory is developed in Section 4.3. In Section 4.4, we propose tools allowing us to give the affirmative interpretations of linguistic negations, and this, by using previous approximation sets. More precisely, we both propose a standard choice of this relation resulting from the neighbourhood relation of nuance meanings and the reference frame of a denied assertion and show how our method works to define these approximations sets when the neighbourhood relation has been computed. We can point out that all examples lead to results in good accordance with the ones linguistically expected. 2 Universe Description We suppose that our discourse universe is characterized by a finite set of concepts. For example, the concepts of height, wage and appearance should be understood as qualifying individuals of the human type. Moreover, each concept can be characterized by a finite set of basic properties having the same description domain. For example, the basic properties small, medium and tall can be associated with the concept height. Finally, linguistic modifiers bearing on these basic properties permit us to express nuanced knowledge, like John is really very tall. This work uses the model proposed in [3] to represent affirmative information expressed in the form x is f α m β P ik or x is not f α m β P ik in the case of negation. In this context, expressing a property like f α m β P ik called here nuanced property, requires a list of linguistic terms. Two ordered sets of modifiers are selected depending on their modifying effects. The first one groups translation modifiers resulting somehow in both a translation and a possible precision variation of the basic property: For example, the set of translation modifiers could be M 7 ={m α α [1..7]}={extremely little, very little, rather little, moderately ( ), rather, very, extremely} totally ordered by the relation: m α <m β α<β. Let us notice that the seven terms have been choosen to define a symmetrical scale in the French language. Unfortunately, this could not correspond exactly to usual adverbs used in English language. The second one consists of precision modifiers which make it possible to increase (or decrease) the precision of the previous properties. For example, F 6 ={f β β [1..6]}={vaguely, neighboring, more or less, moderately ( ), really, exactly} totally ordered by the relation: f α <f β α<β. Within our discourse universe, let us denote as : C the set of distinct concepts C i, D i the domain associated with the concept C i, M the set of modifier combinations, N ik the set of all nuances of the basic property P ik, N i the set of all nuanced properties associated with C i, N the set of all nuanced properties, P i the set of all basic properties associated with C i and P the set of all basic properties.

3 134 D. Pacholczyk, M. Quafafou, and L. Garcia 3 The Reference Frame of a Linguistic Negation The main idea justifying our approach is the fact that the traduction of a negative nuanced property does not correspond to a single positive property due to the vagueness of basic properties and the nuances that are applied. For example, John is not tall does not necessary refers to the sentence John is small but can correspond to several possible interpretations like John is very small, John is small and John is medium. It appears clearly that a modelisation implying denied properties cannot be viewed as a one-to-one correspondence but as a one-to-many one, called here multi-set function. By using linguistic analysis results of linguistic negation ([9] [1], [5], [4]), it has been pointed out in ( [10], [11]) that when one asserts that xisnota then, (1) one rejects a reference to xisa,and(2)ifnecessary, one refers either to another object y (i. e., y is A ) or the logical negation of A, or to another property P different from A (i. e., x is P ) but defined in the same domain, or sometimes to a another nuance of A, or finally to a new basic property denoted as not-a. The previous analysis only defines the standard forms of the linguistic negation. The linguistic negation is defined by using one-to-many mappings from E into P(E) (E parts). Definition 1. A multi-set function is a one-to-many function from E into P(E) (E parts). In this paper we propose a new definition of linguistic negation which can be viewed as a generalization of the one proposed in [13]. For any concept C i, we define the reference frame of a linguistic negation as a parameterized function Ref Neg t. Definition 2. For any concept C i, the reference frame of a linguistic negation is a function Ref Neg t : D i N i P(D i ) P(N i ) defined as follows, knowing that n γ M and t [0.. 5]: - Ref Neg 0 (x,n γ P ik )=(, ), - Ref Neg 1 (x, n γ P ik )=(D i \{x}, {n γ P ik }), - Ref Neg 2 (x, n γ P ik )=({x}, N i \{n γ P ik }), - Ref Neg 3 (x, n γ P ik )=({x}, N ik \{n γ P ik }), - Ref Neg 4 (x, n γ P ik )= ({x}, N i \N ik ), - Ref Neg 5 (x, n γ P ik )=({x}, {not-(n γ P ik )}), where not-(n γ P ik ) is a new basic property associated with C i. Each value of the parameter t is associated with a possible scope of the negation operator, this scope characterizes the reference frame which contains the possible intended positive meanings. It is possible to associate a standard form F t for x is not A with each previous Ref Neg t (x, A). More precisely, when a speaker says x is not A, he means that: - F 0 : For this x, x is A is rejected and there is not any corresponding affirmative expression. For instance, saying Smith is not guilty without reference to affirmative property, may occur in a context where the only thing about his culpability is that his alibi is confirmed. - F 1 : Another object of the same domain satisfies the same nuanced property. Asan example, Jack is not guilty since it is John who is guilty. - F 2 : The same x satisfies another nuance of N i, the set of all nuanced properties associated with C i. For example, John is not small since he is really medium.

4 Optimistic vs. Pessimistic Interpretation of Linguistic Negation F 3 : The same x satisfies another nuance of N ik, the set of all nuances of the basic property P ik. For instance, the doctor can say the temperature is not low because he thinks that the temperature is really low. - F 4 : The same x satisfies a nuance of another basic properties associated with the same concept C i. For instance, the doctor can say cholesterol risk is not low because he thinks that cholesterol risk is medium. - F 5 : The same x satisfies a nuance of a new affirmative basic property. In this case, x is not A means that x is not-a, this new property not-a is associated with the same concept as A. The patient is not seriously ill may introduce a new basic property not-seriously-ill. Remark 1. Note that, in this paper, we are not concerned with the form F 0 (which is scarcely used in knowledge base field) or F 5 (which is in fact an affirmative assertion, and can be directly translated). As noted before, when the user says x is not A, he possibly refers either to another object y (Standard form F 1, y is A ) or to another nuance P ( x is P ) (standard forms F 2 to F 4 ). So, Ref Neg t is defined with the aid of two multi-set functions giving the two main scopes of the negation operator: either an object or a nuanced property is denied. It is obvious that we have: Proposition 1. Ref Neg 4 (x, n γ P ik ) Ref Neg 2 (x, n γ P ik ) and Ref Neg 3 (x, n γ P ik ) Ref Neg 2 (x, n γ P ik ). 4 A New Approach to Linguistic Negation Let us notice that the handling of linguistic negation using the scope proposed in previous section has yet been developped in the framework of fuzzy logic (see [10], [11], [12]). The difference which is made when working with rough set theory is that we do not work on the reference frame but on two approximations of this set (an optimistic one and a pessimistic one). 4.1 Rough Set Theory The theory of rough sets was introduced by Z. Pawlak in the early 1980 s ( [14], [15], [16]). It offers a new tool for the study of vagueness and uncertainty in the context of data analysis. This theory is based on two approximation operators that allow the approximation of a concept, represented as a set, by a pair of sets called lower and upper approximation. Definition 3. Let R be a binary relation on a universe U, r(x)={y U xry}, and X a subset of U. A pair of approximation operators, R and R are defined by : -R (X)={x U r(x) X}, called the lower approximation of X, -R (X)={x U r(x) X }, called the upper approximation of X. Consequently, one may approximate a subset X U by a pair of subsets of U with respect to the binary relation R. The lower approximation of a set X is R (X) and contains elements that necessarily belong to X whereas the upper approximation R (X) contains those that possibly belong to X. This approximation expresses nuanced notion as elements in the lower approximation are referred to as strong members, while elements in the upper approximation are weak members.

5 136 D. Pacholczyk, M. Quafafou, and L. Garcia Definition 4. The system (2 U,,,, R,R ) is called a rough set algebra, where, and are the standard set intersection, union and complement. Proposition 2. Let U be a universe and R U U be a reflexive relation. The approximation operators R,R have the following property: R (X) X R (X). This proposition is a direct deduction from the previous definition of the operators (see Definition 3). In fact, we know that r(x) contains x (reflexivity of R) and r(x) X implies that R (X) X. Similarly, X is contained in its upper approximation R (X) because r(x) X for all x X. Definition 5. A set X is said to be a rough set iff its boundary is not empty R (X) \ R (X). In this situation, the elements that belong to the boundary can not be classified correctly to belong to X nor to its complement X. More details on foundations, methodology and applications of rough set theory are developed in [18]. Different generalizations of rough set theory have been suggested in ([20], [17], [19]). 4.2 Linguistic Negation Reformulation The goal of this section is to reformalize the process we have introduce previously to deal with negation (see Section 3). The input of this process is a sentence with negation that have the following general form x is not A, whereas its output is a set of positive sentences x is Q. Any positive sentence that belongs to the output of the process is a possible interpretation of the negative sentence given as input. Thus, the general goal of the method is to found the set of positive sentences corresponding to an input negative sentence. Let E+ (resp. E-) represents a set of admissible positive (resp. negative) sentences: - E+={(x is n δ P ik ) x D i,n δ M,P ik P i }, and - E-={ (x is not n γ P ik ) x D i,n γ M,P ik P i } The main question is what is the significance of a given negative sentence? The answer is represented as a subset of E+. How to determine this subset? We have introduced our method and process to answer this question. Our approach starts by a first step that rewrite the input negative sentence as a set of nuanced properties. The main problem is next to search a subset of N i that represents the semantic of this negation. Finally we instantiate the subset of nuanced properties to determine the corresponding subset of E+. This subset contains positive sentences that are possible interpretations of a given negative sentence. More formally we can view our process that deals with the linguistic negation as a general function of sets transformation. In fact, we can rewrite, in the case where t = 2, 3, 4, the reference frame of the linguistic negation (see Definition 2) Ref Neg t (x, A) as follows: Ref Neg t (x, A) = ({x}, [Ref Neg t (x,a)]) {x} P(N i ). This approach is characterized by a global view of negation processing as the ultimate goal is to determine the set { x is Q Q [Ref Neg t (x, A)]} that gives a global interpretation of the sentence x is not A. This output set may be vague and difficult to compute. For this reason we refine our method and modify the goal of negation processing. In fact, we only search for an

6 Optimistic vs. Pessimistic Interpretation of Linguistic Negation 137 approximation of linguistic negation and not the set of its global interpretation, as it is the case in the approach using fuzzy context. For this reason, we use the rough set framework ([14], [15], [16]) to formalize the notion of linguistic approximation. 4.3 Linguistic Negation Approximation This section presents an operationalization of the basic concepts of rough sets in the context of linguistic negation. Let us consider λ a binary relation that defines the neighborhood of each basic property P ik or each nuanced property m α of β (P ik ). Now, given a standard form F t (with t {2, 3, 4}) we consider a subset denoted as X t of properties that can be nuanced or not. We define then the two main operators, named lower and upper approximations, and denoted respectively R and R, considering a binary relation λ. Definition 6. Knowing that λ is a binary relation defined on N i, the set of all nuances associated with the concept C i, we denote [p] λ = {q N i pλq }. Definition 7. For any standard form F t with t {2, 3, 4}, let us suppose that λ is a binary relation (at least reflexive) defined on N i, and X t, Y t and T t are subsets of N i such that X t =Y t \T t. Then, a pair of approximation operators, R and R are defined by : -R (X t )={p N i [p] λ X t }, and -R (X t )={p N i [p] λ X t }\T t. Remark 2. The particular set T t contains nuanced properties to be rejected according to the standard form F t of the linguistic negation. For this reason we exclude this set from the solution computed by the upper approximation. This operation is not necessary for the lower operator since we have: R (X t ) X t and T t X t =. Example 1. In the following, for any standard form F t with t {2, 3, 4}, previous sets refer to the definition of [Ref Neg t (x, A)] resulting from the reference frame of the linguistic negation. More precisely, the sets X t and T t are defined as follows: X 2 = N i \{n γ P ik },X 3 = N ik \{n γ P ik },X 4 = N i \ N ik, and T 2 =T 3 = {n γ P ik },T 4 = N ik. These two operators allow us to introduce two main approximations of the negation. Let us now prove the main result giving us the link between each reference frame of a linguistic negation and two precise approximation sets based on rough set theory. Proposition 3. R ([Ref Neg t (x, A)]) [Ref Neg t (x, A)] R ([Ref Neg t (x, A)]), for any standard form F t knowing that t {2, 3, 4}. The inclusion of the lower approximation in [Ref Neg t (x, A)] results from Proposition 2. The inclusion of [Ref Neg t (x, A)] in the upper approximation results also from Propoposition 2 by taking into account the fact that X t T t =[Ref Neg t (x, A)] T t =. Consequently, we have a more flexible interpretation of the negation with a pessimistic operator (R ) which reduces the interpretation of x is not A to only certain nuanced properties when the optimistic one (R ) extends the result to more possible nuanced properties.

7 138 D. Pacholczyk, M. Quafafou, and L. Garcia 4.4 Dealing with Linguistic Negation The goal of this Section is to show the interest of our approach to deal with linguistic negation. More particularly, we point out the relationship between the standard form of negation and the results of the approximation. First, we more emphasize the choice of the binary neighbourhood relation associated with expected linguistic negations. Then, we propose some focusing examples allowing to explain the process of determination of the positive interpretation(s) associated with a negative sentence from its reference frame. Let us propose some tools allowing us to propose a binary relation λ defining the approximation sets R and R leading to the intended meanings of linguitic negations. We search the approximation sets associated with the precise negative assertion x is not n γ P ik for a given standard form F t0 with t0 {2, 3, 4}, knowing also that the other negative assertions x is not n δ P ij are defined for a standard form F t with t {2, 3, 4}. In our context of linguistic negation problem, it is necessary to propose an extension of rough sets by using a neighbourhood (or similarity) relation as model for indiscernibility instead of an equivalence one ([18]): intuitively, knowing (x, n γ P ik ) and the standard form F t0 with t0 {2, 3, 4} of linguistic negation of n γ P ik, n γ P ik and some other nuances rather close to n γ P ik should be viewed as indiscernible. First of all, we suppose that we know, for each nuance n γ P ik, the set of nuances having a meaning rather close to the one of n γ P ik (it results from a linguistic analysis of nuance meaning). So, we obtain a first binary neighbourhood relation ϑ related to the nuance meaning and defined on N i : [p] ϑ ={q N i pϑq}={nuances q having a meaning rather close to the one of p}. Indeed, this relation should be reflexive, certaintly symmetrical, but never transitive. This being so, we have to define another relation λ as model of indiscernibility related now to the process of linguistic negation of n γ P ik for a standard form F t0 with t0 {2, 3, 4}. For previous standard form F t0, this relation defines the reference frame X t0 of the linguistic negation, and the approximation sets R (X t0 ) and R (X t0 ) for the nuance n γ P ik. So, we propose to put in this case [p] λ =[p] ϑ. For other F t,wehavetoavoidas neighbour of n δ P ij, a nuance which can belongs to X t and R (X t ), but being not a certain intended meaning of the linguistic negation. This can be done by choosing in these cases [p] λ equal to p or N i. As a result, λ will also be a neighbourhood relation, since λ will be reflexive, possibly symmetrical, but never be transitive. In the following, knowing previous neighbourhood relation ϑ, we propose a standard neighbourhood relation λ as model of indiscernibility allowing us to define, in all cases, the previous approximation sets R and R. Proposition 4. The neighbourhood relation ϑ related to nuance meaning is supposed well-known. Then, we search R and R the approximation sets associated with the negative assertion x is not n γ P ik for a given standard form F t0 with t0 {2, 3, 4} knowing that the other negative assertions x is not n δ P ij being defined for a precise standard form F t with t {2, 3, 4}. Then, in order to obtain approximations sets in good accordance with linguistic analysis, a new neighbourhood relation λ, dealing with all cases of linguistic negation, can be build as follows: - Case t0=3. For any t 3 we put [n δ P ij ] λ ={n δ P ij }. For t0=3 and t=3, [n γ P ik ] λ (resp. [n δ P ij ] λ ) contains the nuance n γ P ik (resp. n δ P ij ) and none or several other neighbours 1 (if they exist) of n γ P ik (resp. n δ P ij ) related to ϑ. 1 In this paper, we will always choose all neighbours of n γp ik (resp. n δ P ij) related to ϑ.

8 Optimistic vs. Pessimistic Interpretation of Linguistic Negation Case t0=2 (resp.t0=4). For any t 2 (resp. t 4) we put [n δ P ij ] λ =N i. For t0=2 (resp. t0=4) and t=2 (resp. t=4), [n γ P ik ] λ (resp. [n δ P ij ] λ ) contains the nuance n γ P ik (resp. n δ P ij ) and none or several other neighbours, if any, of n γ P ik (resp. n δ P ij ) related to ϑ. In the first case, the X t0 =X 3, the set associated with x is not n γ P ik, contains only all nuances of P ik except P ik. So, no nuance of an other basic property belongs to R (X t0 ), but can belong to R (X t0 ). In other words, the certain affirmative interpretation are nuances of P ik. In the second case, knowing that X t0 cannot be a strict subset of N i, R (X t0 ) cannot contains nuances associated with a standard form F t different from F t0. In other words, the certain interpretations refer only to nuances belonging to X t0, the reference frame of x is not n γ P ik. It appears that this relation leads, in all cases, to results in good accordance with the intended meanings of the linguistic negations. The following examples illustrate the use of the relation λ. Example 2. Let us suppose that the set D i ={John, Jack} and P i ={P i1,p i2 }={visible (in the crowd), invisible (in the crowd)}. We do not use modifiers. The relation υ is defined as follows : [visible] ϑ ={visible}, [invisible] υ = {invisible}. We suppose that F 4 is associated with x is not visible and x is not invisible. So, we have: [visible] λ ={visible}, [invisible] λ = {invisible}. Then, the approximation of John is not visible gives us: X 4 ={invisible}, R (X 4 )=R (X 4 )={invisible} and the approximation of Jack is not invisible : X 4 ={visible},r (X 4 )=R (X 4 )={visible}. So, John is invisible and Jack is visible are certain interpretations of previous linguistic negations. Example 3. In this example, we consider the sets D i ={John, Jack, Tom} and P i ={P i1, P i2,p i3 }={small, medium, tall}. We consider only one modifier called very applied to the basic properties small and tall. So, N i ={very P i1,p i1,p i2,p i3, very P i3 }.We suppose now that we know the standard form F t associated with the linguistic negation. So, x is not very P i1, x is not P i1, x is not P i2, x is not P i3, x is not very P i3 are respectively connected with standard forms F 3,F 4,F 2,F 4 and F 3. The natutal neighbourhood relation υ is defined as follows: in all cases, [p] ϑ = {p}. We can now define explicitely the binary relation λ, by using previous results about particular cases of linguistic negation. - Approximation of x is not very P i1. We obtain: for any p, [p] λ ={p}. Then, X 3 = {P i1 } leads to R (X 3 )={P i1 } and R (X 3 )={P i1 }. So, x is small is the certain linguistic negation of x is not very small. - Approximation of x is not very P i3. This symmetrical case gives us: X 3 ={P i3 } leads to R (X 3 )={P i3 } and R (X 3 )={P i3 }. So, x is tall is the certain linguistic negation of x is not tall. - Approximation of x is not P i1. We can have: [very P i1 ] λ =N i, [P i1 ] λ ={P i1 }, [P i2 ] λ =N i, [P i3 ] λ ={ P i3 }, [very P i3 ] λ =N i. So, X 4 ={P i2, P i3, very P i3 } leads to R (X 4 )={P i3 } and R (X 4 )={P i2,p i3, very P i3 }. So, x is tall is a certain linguistic negation of x is not small. Moreover, x is medium and x is very tall are possible but not certain interpretations. - Approximation of x is not P i3. This symmetrical case gives us: X 4 ={P i2,p i1, very P i1 } leads to R (X 4 )={P i1 } and R (X 4 )={P i1,p i2, very P i1 }. So, x is small is a certain linguistic negation of x is not tall, and x is medium or x is very small are possible but not certain interpretations. - Approximation of x is not P i2. We obtain: [very P i1 ] λ =N i,[p i1 ] λ =N i,[p i2 ] λ ={P i2 }, [P i3 ] λ =N i, [very P i3 ] λ =N i. Then, X 2 ={very P i1,p i1,p i3, very P i3 } leads to R (X 2 )=

9 140 D. Pacholczyk, M. Quafafou, and L. Garcia and R (X 2 )={very P i1,p i1,p i3, very P i3 }. It appears that no certain interpertation of x is not medium exists, but four affirmative interpretations are possible. It appears clearly that this standard neighbourhood relation λ leads to results in good accordance with the ones linguistically expected. Note that we are currently studying other plausible relations λ and its basic properties, like the ones proposed in ([17], [18]). The examples presented in this Section give an idea on the quality of the results computed by the approximation according to each standard form of negation (due to lack of space, we do not present more examples). More refined relations can be used to analyze the behavior of the approximations. We can point out the fact that the upper approximation R contains all possible interpretations of each linguistic negation, but can also contain some neighbours very close to these expected solutions. Moreover, the lower approximation R only contains nuances acceptable for the standard form associated with this linguistic negation. So, among the elements of the approximation sets, several of them are certain or possible interpretations, and others are neighbours of them but not plausible. Then, it is necessary to propose a default strategy of choice of suitable affirmative interpretations. Unfortunately, due to the lack of space, we do not present these several strategies. 5 Conclusion This paper deals with a new approach of the problem of linguistic negation. Clearly, it consists in finding, for a given negative sentence, the suitable positive translations that are linguistically expected. The originality of the work is that we do not compute the reference frame of positive translations associated with the negative sentence but we work on two approximation sets of the reference frame: a pessimistic one gives the translations which are sure and an optimistic one gives all the possible translations. These sets being computed, we propose a standard choice of the relation of neighbourhood. The last step, which is not studied here, is the choice strategy of one (or several) affirmative interpretation(s) associated with a negative sentence from its reference frame. These strategies leads to select positive translations that are in good accordance with the ones linguistically expected. References 1. Culioli, A.: Pour une linguistique de l énonciation : Opérations et Représentations, Tome 1, Ophrys 2ds., Paris, Dermott D., Tarskian Semantics, or no Notation Without denotation, Cognitive Science 2(3), , Desmontils, E., Pacholczyk, D.: Towards a linguistic processing of properties in declarative modelling, Int. Jour. CADCAM and Computer Graphics, 12:4, , Ducrot, O., Schaeffer J. -M.et al.: Nouveau dictionnaire encyclopédique des sciences du langage. Eds. du Seuil, Paris, Horn, L.R.: A Natural History of Negation. The University of Chicago Press, McCawley J. D., Everything That linguists Have always Wanted to Know about Logic (2nd ed.), Chicago Univ. Press, 1993.

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