Fuzzy Quantifiers: A Computational Theory PDF
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IngoGlöckner FuzzyQuantifiers StudiesinFuzzinessand SoftComputing, Volume193 Editor-in-chief Prof.JanuszKacprzyk SystemsResearchInstitute PolishAcademyofSciences ul.Newelska6 01-447Warsaw Poland E-mail:[email protected] Furthervolumesofthisseries Vol.186.RadimBeˇlohlávek,Vilém canbefoundonourhomepage: Vychodil FuzzyEquationalLogic,2005 springer.com ISBN3-540-26254-7 Vol.187.ZhongLi,WolfgangA.Halang, Vol.178.ClaudeGhaoui,MituJain, GuanrongChen(Eds.) VivekBannore,LakhmiC.Jain(Eds.) IntegrationofFuzzyLogicandChaos Knowledge-BasedVirtualEducation,2005 Theory,2006 ISBN3-540-25045-X ISBN3-540-26899-5 Vol.179.MirceaNegoita, Vol.188.JamesJ.Buckley,LeonardJ. BerndReusch(Eds.) Jowers RealWorldApplicationsofComputational SimulatingContinuousFuzzySystems,2006 Intelligence,2005 ISBN3-540-28455-9 ISBN3-540-25006-9 Vol.189.HansBandemer Vol.180.WesleyChu, MathematicsofUncertainty,2006 TsauYoungLin(Eds.) ISBN3-540-28457-5 FoundationsandAdvancesinDataMining, 2005 Vol.190.Ying-pingChen ISBN3-540-25057-3 ExtendingtheScalabilityofLinkage LearningGeneticAlgorithms,2006 Vol.181.NadiaNedjah, ISBN3-540-28459-1 LuizadeMacedoMourelle FuzzySystemsEngineering,2005 Vol.191.MartinV.Butz ISBN3-540-25322-X Rule-BasedEvolutionaryOnlineLearning Systems,2006 Vol.182.JohnN.Mordeson, ISBN3-540-25379-3 KiranR.Bhutani,AzrielRosenfeld FuzzyGroupTheory,2005 Vol.192.JoseA.Lozano,PedroLarrañaga, ISBN3-540-25072-7 IñakiInza,EndikaBengotxea(Eds.) TowardsaNewEvolutionaryComputation, Vol.183.LarryBull,TimKovacs(Eds.) 2006 FoundationsofLearningClassifierSystems, ISBN3-540-29006-0 2005 ISBN3-540-25073-5 Vol.193.IngoGlöckner FuzzyQuantifiers:AComputationalTheory, Vol.184.BarryG.Silverman,AshleshaJain, 2006 AjitaIchalkaranje,LakhmiC.Jain(Eds.) ISBN3-540-29634-4 IntelligentParadigmsforHealthcare Enterprises,2005 ISBN3-540-22903-5 Vol.185.SpirosSirmakessis(Ed.) KnowledgeMining,2005 ISBN3-540-25070-0 Ingo Glöckner Fuzzy Quantifiers A Computational Theory ABC Dr.IngoGlöckner FernUniversitätinHagen LGPrakt.Inf.VII Informatikzentrum Universitätsstr.1 58084Hagen Germany E-mail:[email protected] LibraryofCongressControlNumber:2005936355 ISSNprintedition:1434-9922 ISSNelectronicedition:1860-0808 ISBN-10 3-540-29634-4SpringerBerlinHeidelbergNewYork ISBN-13 978-3-540-29634-8SpringerBerlinHeidelbergNewYork Thisworkissubjecttocopyright.Allrightsarereserved,whetherthewholeorpartofthematerialis concerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation,broadcasting, reproductiononmicrofilmorinanyotherway,andstorageindatabanks.Duplicationofthispublication orpartsthereofispermittedonlyundertheprovisionsoftheGermanCopyrightLawofSeptember9, 1965,initscurrentversion,andpermissionforusemustalwaysbeobtainedfromSpringer.Violationsare liableforprosecutionundertheGermanCopyrightLaw. SpringerisapartofSpringerScience+BusinessMedia springer.com (cid:1)c Springer-VerlagBerlinHeidelberg2006 PrintedinTheNetherlands Theuseofgeneraldescriptivenames,registerednames,trademarks,etc.inthispublicationdoesnotimply, evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevantprotectivelaws andregulationsandthereforefreeforgeneraluse. Typesetting:bytheauthorandTechBooksusingaSpringerLATEXmacropackage Printedonacid-freepaper SPIN:11430025 89/TechBooks 543210 Preface From a linguistic perspective, it is quantification which makes all the differ- ence between “having no dollars” and “having a lot of dollars”. And it is the meaning of the quantifier “most” which eventually decides if “Most Ameri- cans voted Kerry” or “Most Americans voted Bush” (as it stands). Natural language(NL)quantifierslike“all”,“almostall”,“many”etc.serveanimpor- tant purpose because they permit us to speak about properties of collections, as opposed to describing specific individuals only; in technical terms, quan- tifiers are a ‘second-order’ construct. Thus the quantifying statement “Most AmericansvotedBush”assertsthatthesetofvotersofGeorgeW.Bushcom- prisesthemajorityofAmericans,while“Bushsneezes”onlytellsussomething about a specific individual. By describing collections rather than individuals, quantifiers extend the expressive power of natural languages far beyond that of propositional logic and make them a universal communication medium. Hencelanguageheavilydependsonquantifyingconstructions.Theseoften involvefuzzyconceptslike“tall”,andtheyfrequentlyrefertofuzzyquantities in agreement like “about ten”, “almost all”, “many” etc. In order to exploit this expressive power and make fuzzy quantification available to technical applications, a number of proposals have been made how to model fuzzy quantifiers in the framework of fuzzy set theory. These approaches usually reduce fuzzy quantification to a comparison of scalar or fuzzy cardinalities [197, 132]. Thus for understanding a quantifying proposition like “About ten persons are tall”, we must know the degree to which we have j tall people in thegivensituation,andthedegreetowhichthecardinalj qualifiesas“about ten”. However, the results of these methods can be implausible in certain cases[3,35,54],i.e.theycanviolatesomebasiclinguisticexpectations.These problems certainly hindered the spread of the ‘traditional’ approaches into commercial applications. This monograph proposes a new solution which shows itself immune against the deficiencies of existing approaches to fuzzy quantification. The theory covers all aspects of fuzzy quantification which are necessary for a comprehensive treatment ofthe subject,ranging fromabstracttopics like the VI Preface proper semantical description of fuzzy quantifiers to practical concerns like robustness of interpretations and efficient implementation. The novel theory of fuzzy quantification is • a compatible extension of the Theory of Generalized Quantifiers [7, 9]; • not limited to absolute and proportional quantifiers; • a genuine theory of fuzzy multi-place quantification; • not limited to finite universes of quantification; • not limited to quantitative (automorphism-invariant) examples of quanti- fiers; • based on a rigid axiomatic foundation which enforces meaningful and co- herent interpretations; • fully compatible with the formation of negation, antonyms, duals, and other constructions of linguistic relevance. To accomplish this, the theory introduces • a formal framework for analysing fuzzy quantifiers; • asystemofformalpostulatesforthosemodelsoffuzzyquantificationthat are semantically conclusive; • a number of theorems showing that these models are plausible from a linguistic point of view and also internally coherent; • adiscussionofseveralconstructiveprinciplesalongwithananalysisofthe generated models; • acollectionofalgorithmsforimplementingfuzzyquantifiersinthemodels and examples for their application. Specifically,thisworkapproachesthesemanticsoffuzzyNLquantificationby formalizing the essential linguistic expectations on plausible interpretations, and it also presents examples of models which conform to these semantical requirements. Thebookfurtherdisclosesageneralstrategyforimplementing quantifiers in a model of interest, and it develops a number of supplementary methods which will optimize processing times. In order to illustrate these techniques, the basic procedure will then be detailed for three prototypical models, and the complete algorithms for implementing the relevant quantifiers in these models will be presented. Hence the proposed solution is not only theoret- ically appealing, but it also avails us with computational models of fuzzy quantification which permit its use in practical applications. The quantifiers covered by the new algorithms include the familiar absolute and proportional types known from existing work on fuzzy quantification, but they also com- prise some other cases of linguistic relevance. In particular, the treatment of quantifiers of exception (“all except k”) and cardinal comparatives (“many more than”) is innovative in the fuzzy sets framework. The material presented in the book will be of interest to those working at thecrossroadsofnaturallanguageprocessingandfuzzysettheory.Therange of applications comprises fuzzy querying interfaces, where fuzzy quantifiers Preface VII support more flexible queries to information systems; systems for linguistic data summarization, where fuzzy quantifiers are used to build succinct lin- guistic summaries of given databases; decision-support systems, where fuzzy quantifiers implement multi-criteria decisionmaking, and many others. In all of these applications, the ‘old’ models of fuzzy quantification can easily be replaced with the improved models developed here. I hope that by removing the ‘plausibility barrier’ faced by earlier techniques and rendering possible a more reliable use of fuzzy quantifiers, the new theory of fuzzy NL quantifica- tion will contribute to a break-through of these techniques into commercial applications.∗ Hagen September 2005 Ingo Glo¨ckner ∗ The monograph is based on the author’s PhD thesis titled “Fuzzy Quantifiers inNaturalLanguage:SemanticsandComputationalModels” [52]whichwaspub- lished by Der Andere Verlag, Osnabru¨ck (now T¨onning), Germany under ISBN 3-89959-205-0. Kind permission of Der Andere Verlag is acknowledged to repub- lish this improved version of the manuscript. Overview of the Book Thebookisorganizedasfollows.Thefirstchapter presentsageneralintroduc- tionintothehistoryandmainissuesoffuzzynaturallanguagequantification. Tothisend,thebasiccharacteristicsoflinguisticquantifiersarefirstreviewed inordertodistillsomerequirementsonaprincipledtheoryoflinguisticquan- tification. Following this, we consider the problem of linguistic vagueness and its modelling in terms of continuous membership grades, which is fundamen- tal to fuzzy settheory. Zadeh’s traditional framework for fuzzy quantification [197, 199] is then explained. It is argued that the framework is too narrow for a comprehensive description of fuzzy NL quantification, and the partic- ular approaches that evolved from it are shown to be inconsistent with the linguistic data. The second chapter introduces a novel framework for fuzzy quantifica- tion in which the linguistic phenomenon can be studied with the desired comprehensiveness and formal rigor. The framework comprises: fuzzy quan- tifiers, which serve as the operational model or target representation of the quantifiers of interest; semi-fuzzy quantifiers, which avail us with a uniform and practical specification format; and finally QFMs (quantifier fuzzification mechanisms), which establish the link between specifications and operational quantifiers.Thebasicrepresentationsunderlyingtheproposedframeworkare directlymodelledafterthegeneralizedquantifiersfamiliarinlinguistics[7,9]. Comparedtothetwo-valuedlinguisticconcept,semi-fuzzyquantifiersaddap- proximate quantifiers like “almost all”, while fuzzy quantifiers further admit fuzzyarguments(“tall”,“young”etc.).Thus,semi-fuzzyquantifiersandfuzzy quantifiers are the apparent extension of the original generalized quantifiers to Type III and Type IV quantifications in the sense of Liu and Kerre [108]. The organization of my approach into specification and operational layers greatly simplifies its application in practice. The commitments intrinsic to this analysis of fuzzy quantification are discussed at the end of the chapter, which also judges the actual coverage of the proposed approach compared to the phenomenon of linguistic quantification in its full breadth. X Overview of the Book The third chapter is concerned with the investigation of formal criteria whichcharacterizeaclassofplausiblemodelsoffuzzyquantification.Theba- sicstrategycanbelikenedtotheaxiomaticdescriptionoft-norms[148],which constitutetheplausibleconjunctionsinfuzzysettheory.Thusmyapproachis essentially algebraic. The basic idea is that of formalizing the intuitive expec- tations on plausible interpretations in order to avoid the notorious problems ofexistingapproaches.Tothisend,thechapterintroducesasystemofsixba- sic requirements which distill a larger catalogue of linguistic desiderata. The criteria are chosen such that they capture independent aspects of plausibil- ity and that, taken together, they identify a class of plausible models which answers the relevant linguistic expectations. The fourth chapter gives evidence that the proposed axioms indeed cap- ture a class of plausible models. To this end, an extensive list of criteria will be considered which are significant to the linguistic plausibility of the com- puted interpretations and to their expected coherence. All of these criteria are validated by the proposed models. This supports my choice of axioms even if some of these might appear rather ‘abstract’ at first sight. Apart from this purpose of justifying the proposed class of models, the formalization of semantical criteria is also a topic of independent interest. By investigating such criteria, we can further our knowledge about quantifier interpretations in natural languages. The space of possible models will be further explored in the fifth chapter. Specificallywewillpayattentiontocertainsubclassesofmodels,i.e.classesof models with some common structure or joint properties. The relative homo- geneity of the models within these classes permits the definition of important concepts e.g. regarding the specificity of results. We shall further identify the classofstandardmodelswhichcomplywiththestandardchoiceofconnectives in fuzzy set theory. The role of these standard models to fuzzy quantification can be likened to that of Abelian groups vs. general groups in mathematical group theory. The sixth chapter, then, is devoted to the study of some additional prop- erties, like continuity (smoothness), which are ‘nice to have’ from a practical perspective,butnotalwaysusefulfortheoreticalinvestigations,orsometimes even awkward in this context. This is why these properties should not be in- cluded into the core requirements on plausible models, but they can of course be used to further restrict the considered models if so desired. The chapter further discusses some ‘critical’ properties which cannot be satisfied by the models because they contradict some of the core requirements. (usually these properties even fail in much weaker systems). The existence of such cases is not surprising, of course, because fuzzy logic, as a rule, can never satisfy all axiomsofBooleanalgebra.Thesedifficultieswillusuallyberesolvedbyshow- ing that the critical property conflicts with very basic requirements, and by pruning the original postulate to a compatible ‘core’ requirement. Having laid these theoretical foundations, we shall proceed to the issue of identifying prototypical models, which are potentially useful to applications. Overview of the Book XI The investigation of various constructive principles will give us a grip on such concrete examples. Existing research has typically tried to explain fuzzy quantification in terms of cardinality comparisons, based on some notion of cardinalityforfuzzysets.However,suchreductionisnotpossibleforarbitrary quantifiers.Henceacomprehensiveinterpretationoflinguisticquantifiersmust rest on a more general conception. Chaptersseventotenwilldescribesomesuitablechoicesforsuchconstruc- tions which result in the increasingly broader classes of MB-models (chap- ter seven), F -models (chapter eight), and finally F or F -models (chapter ξ Ω ω nine). All of these models rest on a generalized supervaluationist approach based on a three-valued cutting mechanism. The tenth chapter, by contrast, will present a different mechanism based on the extension principle, which results in the constructive class of F -models. This class of models which are ψ definableintermsofthestandardextensionprinciple,isthenshowntoprovide a different perspective on the class of F -models, to which it is coextensive. Ω Apart from introducing these classes of prototypical models, it will also be shown how how important properties of the models, like continuity, can be expressed in terms of conditions imposed on the underlying constructions. This facilitates the test whether a model of interest is sufficiently robust, its comparison of models with respect to their specificity etc. In particular, this analysis reveals that all practical F - or F -models belong to the F -type. ψ Ω ξ The eleventh chapter develops the algorithmic part of the theory, and is thus concerned with the issue of efficient implementation. Obviously, it only makes sense to consider practical (i.e. sufficiently robust) models. Thus, we can confine ourselves to analyzing models of the F -type. The general strate- ξ gies for efficient implementation described in this chapter will be instantiated for three prototypical models. The considered quantifiers include the familiar absolute and proportional types, as well as quantifiers of exception and car- dinal comparatives. Some application examples are also discussed at the end of the chapter. The twelvth chapter proposes an extension of the basic framework for fuzzy quantification. The generalization will cover the most powerful notion of quantifiers developed by mathematicians, so-called Lindstr¨om quantifiers [106].Thesequantifiersarealsoofpotentiallinguisticrelevance,andIexplain how they can be used to model certain reciprocal constructions in natural language which give rise to branching quantification. The thirteenth chapter will resume the main contributions of this work and propose some directions for future research. There are two appendices. AppendixApresentsthestudyofexistingapproachescitedintheintroduc- tion. It proposes an evaluation framework for approaches to fuzzy quantifica- tion based on Zadeh’s traditional framework. This analysis makes it possible toapplythesemanticalcriteriadevelopedinthemainpartofthebooktothe existing approaches to fuzzy quantification described in the literature.
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