the cognitivistic approach presupposes the existence of the external world, structured by given objects and properties the internal representations of which are to be used by cognitive systems in order to act and react; the emergent approach is described as based on the model concept of self-organization with the cognitive system constantly adapting to changing environmental conditions by modifying its internal representation of them. Whereas both these approaches appear to be based on the traditional rationalistic paradigm of mind-matter-duality-static the former, dynamic the latter-the third category or the enactive approach is characterized as being based upon the notion of structural coupling. It dispenses with the assumed distinction of an external world and an internal representation of it as believed to constitute the individuated self, but considers instead mutually structured coupling the fundamental condition prior to and underlying any discernment between self and world, subject and object, the cognitive system and its environment, etc. Hence, the process of cognition and the procedural results of it appear to become indistinguishable (enaction), allowing meaning to emerge spontaneously, variable, and vague according to the history of constraints embodied by structured connections between an organism and its environment.
According to these categories of cognitive modelling, fuzzy computational semantics tries to model meaning enactively, reconstructing procedurally both, the significance of entities and the meanings of signs as a function of a first and second order semiotic embedding relation of situations (or contexts) and of language games (or cotexts).
There is some chance for doing so because in linguistics we do not have to start cognitively ab ovo. Taking human beings as the most efficient natural SCIPS with high performance symbol manipulation and understanding capabilities, natural language provides a cognitively interesting meaning representation system whose outstanding structuredness in the aggregated form of texts in discourse situations may serve as guidelines rarely observed yet. In doing so, however, it is necessary to pass from traditional approaches in linguistics proper that analyse introspectively the propositional contents of singular sentences as conceived by idealized speakers on to approaches based upon the empirically well founded observation and rigourous mathematical description of global regularities in masses of texts produced by real speakers in actual situations of either performed or intended communication.
it has to be realized that there are certain entities in the world which are (or become) signs and have (or acquire) interpretable meaning in the sense of signifying something else they stand for, beyond their own physical existence (whereas other entities do not). it has to be explored how these (semiotic) entities may be constituted and how the meaning relation be established on the basis of which regularities of observables (uniformities), controlled by what constraints, and under which boundary conditions of pragmatic configuration of communicative interactions (situations). it has to be answered why some entities may signify others by serving as labels for them (or rather by the meanings these labels purport), instead of being signified semiotically by way of positions, load values and/or states distributed over a system of semiotic/non-semiotic entities. These allow for distinctions of different distributional patterns being made, not however, for representing the patterns by different (symbolic) labels.
In doing so, a semiotic paradigm will have to be followed which hopefully may allow to avoid (if not to solve) a number of spin-off problems, which originate in the traditional distinction and/or the methodological separation of the meaning of a language's term from the way it is employed in discourse. It appears that failing to mediate between these two sides of natural language semantics, phenomena like creativity, dynamism, efficiency, vagueness, and variability of meaning-to name only the most salient-have fallen in between, stayed (or be kept) out of the foci of interest, or were being overlooked altogether, sofar. Moreover, the classical approach in formal theory of semantics which is confined to the sentence boundary of propositional constructions, is badly in want of operational tools to bridge the gap between formal theory of language description (competence) and empirical analysis of language usage (performance) that is increasingly felt to be responsible for the unwarranted abstractions of fundamental properties of natural languages.
In a rather sharp departure from more traditional ways of introspective analyses, our empirical approach in quantitative linguistics (QL) accepts the complex ontological status of natural languages in its aggregated form of texts as compiled in large, pragmatically homogeneous, linguistic corpora. Accordingly, the textual analyses will be concerned with entities whose first order situational significance appears to be identical with their being signs, aggregates, and structures thereof, and whose second order situational significance allows for their semantic interpretatibility as constituted by their being an instatiation of some language game. Therefore, word meaning may well be reconstructable as a function of the elastic constraints which these two levels of semiotic embedding impose on natural language texts constituting the structural coupling between the language users and the meanings they understand.
From the semiotic point-of-view any identification and interpretation of external structures has to be conceived as some form of information processing which (natural/artificial) systems-due to their own structuredness-are able to perform. These processes or the structures underlying them ought to be derivable from rather than presupposed to procedural models of meaning. Other than so-called knowledge-based approaches to cognitive tasks and natural language understanding employed sofar in information processing systems that artificial intelligence research (AI) or computational linguistics (CL) have advanced, it is argued here that meaning need not be introduced as a presupposition of semantics but may instead be derived as a result of procedural modelling3. The present approach is based upon a phenomenological (re)interpretation of the formal concept of situation and the analytical notion of language game. The combination of both lends itself easily to operational extensions in empirical analysis and procedural simulation of associative meaning constitution which grasps essential parts of what Peirce named semiosis 4.
By recognizing these invariants and by mapping them as uniformities across situations, cognitive systems properly attuned to them are able to identify and understand those bits of information which appear to be essential to form these systems' particular view of reality: a flow of types of situations related by uniformities like e.g. individuals, relations, and time-space-locations. These uniformities constrain a system's external world to become its reality as its specific fragments of persistent courses of events whose expectability renders them interpretable.
In semiotic sign systems like natural languages, such uniformities also appear to be signalled more basically by word-types whose employment as word-tokens in texts exhibit a special form of structurally conditioned constraints. Not only allows their use the speakers/hearers to convey/understand meanings differently in different discourse situations (efficiency), but at the same time the discourses' total vocabulary and word usages also provide an empirically accessible basis for the analysis of structural (as opposed to referencial) aspects of event-types and how these are related by virtue of word-uniformities accross phrases, sentences, and texts uttered. Thus, as a means for the intensional (as opposed to the extensional) description of (abstract, real, and actual) situations, the regularities of word-usages may serve as an access to and a representational format for those elastic constraints which underly and condition any word's linguistic meaning, the interpretations it allows within possible contexts of use, and the information its actual employment on a particular occasion may convey.
Owing to Barwise's/ Perry's new approach-and notwithstanding its traditional (mis)conception as duality (i.e. the independent sign-meaning-view) of an information-processing system on the one hand which is confronted on the other hand with a prefixed external reality whose accessible fragments are to be recognized as its environment-this notion of situation proves to be pivotal for an empirical extension to their theory of semantics. Not only can it be employed to devise a procedural model for the situational embeddedness of cognitive systems as their primary means of mutual accessability6, but also does it allow to capture the semiotic unity as specified by Wittgenstein in his notion of language games 7 or the contextual (i.e. the usage-meaning-view).
In the sequel we will outline a feasible approach to have the meaning function's range being computed as a result of exactly those cognitive procedures by way of which structuredness emerges and understanding is produced from observing and analyzing the domain's regular constraints as imposed on the linear ordering (syntagmatics) and the selective combination (paradigmatics) of natural language items in texts produced in communicative performance. It will be modelled as a multi-level dynamic description which reconstructs the structural connections (couplings) of possible expressions towards the semiotic cognitive information processing systems (that may both intend/produce and realize/understand them) in respect to their situational settings, being specified by the expressions' pragmatics.
Based upon the fundamentals of semiotics, the philosophical concept of communicative language games as specified by the formal notion of situations, not only allows for the formal identification of both, the (internal) structure of the cognitive subject with the (external) structure of its environment, but-being tied to the observables of actual language performance-opens up an empirical approach to procedural word semantics. Whatever can formally been analyzed as uniformities in Barwiseian discourse situations may be specified by word-type regularities as determined by co-occurring word-tokens in pragmatically homogeneous samples of language games. Going back to the fundamentals of structuralistic descriptions of regularities of syntagmatic linearity and paradigmatic selectivity of language items, the correlational analyses of discourse will allow for a two-level word meaning and world knowledge representation whose dynamism is a direct function of elastic constraints established and/or modified in communicative interaction by use of linguistic signs in language performance.
Implemented, such a system will eventually lead to something like machine-simulated cognition, letting information be processed as a means of perceiving a (virtual) reality from its (textual) environment which is accessible through and structured by world-revealing (linguistic) elements of communicative sign usage. For natural language semantics this is tantamount to (re)present a term's meaning potential by a fuzzy distributional pattern of the modelled system's state changes rather than a single symbol whose structural relations are to represent the system's interpretation of its environment. Whereas the latter has to exclude, the former will automatically include the (linguistically) structured, pragmatic components which the system will both, embody and employ as its (linguistic) import to identify and to interpret its environmental structures by means of its own structuredness.
Operationalizing the Wittgensteinian notion of language games and drawing on his assumption that a great number of texts analysed for the terms' usage regularities will reveal essential parts of the concepts and hence the meanings conveyed11, such a description turns out to be identical with a analytical procedure. Starting from the universal constraints known to be valid for all natural languages, the present approch captures and operationalizes the restrictions which hold both for the syntagmatic and the paradigmatic relations of linguistic units observed.
These constraints may be formalized as sets of fuzzy subsets of the vocabulary employed. Represented as a set-theoretical system of meaning points, the regularities detected will depict the distributional character of word meanings in an elastic mode of mutual constraints. Being composed of a number of operationally defined elements whose varying contributions can be identified with values of the respective membership functions, these can be derived from and specified by the differing usage regularities that the corresponding lexical items have produced in discourse. This translates the Wittgensteinian notion of meaning into an operation that may be applied empirically to any corpus of pragmatically homogeneous texts constituting a language game.
Based upon the distinction of the syntagmatic and paradigmatic relatedness of language items in discourse, the core of the representational formalism can be characterized as a two-level process of abstraction (called a- and d-abstraction) providing the set of usage regularities and the set of meaning points of those word-types which are being instantiated by word-tokens as employed in natural language texts. The resultant structure of these constraints render the set of potential interpretations which are to be modelled in the sequel as the semantic hyperspace structure (SHS ).
A modified correlation coefficient has been used as a first mapping function a. It allows to compute the relational interdependence of any two lexical items from their textual frequencies. For a text corpus
of pragmatically homogeneous discourse, having an overall length
As a fuzzy binary relation,
By identifying zn Î S with the numerically specified elements of potential paradigms, the set of possible combinations S ×S may structurally be constrained and evaluated without (direct or indirect) recourse to any pre-existent external world. Introducing a Eucledian metric
As a result of the two consecutive mappings (Tab. 1),
any meaning point's position in SHS is determined by all the
differences (d- or distance-values) of all regularities
of usage (a- or correlation-values) each lexical item shows
against all others in the discourse analysed. Thus, it is the basic
analyzing algorithm which-by processing natural language texts-provides
the processing system with the ability to recognize and represent and to
employ and modify the structural information available to the system's
performance constituting its understanding.
This answers the question where the label
in our representation come from: put into a discourse environment,
the system's text analyzing algorithm provides the means how the
topological position of any meaning point z Î áS,¶ñ
is identified and labeled by a vocabulary item x Î V according to the
two consecutive mappings which can formally be stated as a composition of
the two restricted relations [(d)\tilde] | y and
[(a)\tilde] | x (Fig. 1).
It is achieved without recurring to any investigator's or his test-persons'
word or world knowledge (semantic competence), but solely on
the basis of usage regularities of lexical items in discourse which are
produced by real speakers/hearers in actual or intended acts of
communication (communicative performance).
Following the semiotic notion of understanding and
meaning constitution, the SHS-structure may be considered the core
of a two-level conceptual knowledge representation
system12. Essentially,
it separates the format of a basic (stereotype) word meaning representation
from its latent (dependency) relational concept organization. Whereas the
former is a rather static, topologically structured (associative) memory,
the latter can be characterized as a collection of dynamic and flexible
structuring procedures to re-organize the memory data by semiotic principles
under various aspects13.
SHS being a distance-ralational data structure, well-known algorithmic
search strategies cannot immediately be made to work. They are mostly
based upon some non-symmetric relational structure as e.g. directed
graphs in traditional meaning and knowledge represenation formats.
To convert the SHS-format into such a node-pointer-type structure, the
SHS-model has to be considered as conceptual raw data or associative base
structure which particular procedures may operate on to reorganize it.
This is achieved by a recursively defined procedure
that produces tree-structured hierarchies of meaning points under given
aspects according to and in dependence of their meanings' relevancy.
This so-called D-operation has been conceived as a modified
derivative of a
minimal spanning tree-algorithm14.
The procedure is recursively defined
to operate on the semantic hyper space data zn Î áS,¶ñ. Given one meaning point's position as
a start, the algorithm will work its way through all labeled
points-unless stopped under conditions of a given target
node, number of nodes to be processed, or threshold of maximal
distance-transforming prevailing similarities of paradigms
as represented by adjacency of points to induce a binary,
non-symmetric, and transitive relation of lexical relevance
between them. This relation allows for the hierarchical reorganization
of meaning points as nodes under a primed head in an n-ary tree called
dispositional dependency structure (DDS)15.
It is tantamount to a numerical assessment
(criterialty)16 and a
hierarchical re-structuring (tree) of elements under a head point's
aspect according to the dependency relation between descendant points
along which activation might spread in case of the head point's stimulation.
To illustrate the feasibility of the D-operation's generative procedure, a subset of the relevant, linguistic
constraints triggered by the lexical item
xi, i = COMPUTER/computer
is given in the format of a weighted semantic DDS
Fig.. It has been generated by
the procedure described from the SHS-data as computed from the corpus
of German newspaper texts17.
Weighted numerically as a function
of an element's distance values and its associated node's level and
position in the tree, DDS(zi) either is an expression of the
head-node's zi meaning-dependencies on the daughter-nodes zn
or, inversely, expresses their meaning-criterialities adding up to
an aspect's interpretation determined by that head.
For a wide range of purposes in processing DDS-trees,
differing criterialities of nodes can be used to estimate which paths are more
likely being taken against others being followed less likely under
priming activated by certain meaning points.
Thus, actual and potential (human) problemsolvers feel the
increasing need to employ computers more
effectively than hitherto for informational search through
masses of natural language material. Although the demand is high for
intelligent machinery to assist in or even provide speedy and reliable
selection of relevant information under individual aspects of interest
within specifyable subject domains, such systems are not yet available.
Suppose we have an information processing system with an initial
structure of constraints modelled as SHS . Provided the system
is exposed to natural language discourse and capable of basic
structural processing as postulated, then its (rudimentary) interpretations
generated from given texts will not change its subsequent
interpretations via altered input-cycles, but the system will come
up with differing interpretations due to its modified old and/or
established new constraints as structural properties of
processing. Thus, it is the structure that determines the
system's interpretations, and being subject to changes according to
changing environments of the system, constitutes its autopoetic
space 20.
Considering a text understanding system as SCIPS and letting its
environment consist of texts being sequences of words, then the system
will not only identify these words but-according to its own capacity for
a- and d-abstraction together with its D-operation-will
at the same time realize the semantic connectedness between their
meanings which are the system's state changes or dispositional
dependencies that these words invoke. They will, however, not only
trigger DDS but will at the same time-because of the prototypical or
distributed representational SHS format being separated from
the dynamic DDS organization of meaning points-modify the underlying
data according to recurrent syntagmatic
and paradigmatic structures as detected from the textual
environment21.
Dispositional dependencies appear to be a prerequisit not only to
source-oriented,
contents-driven search and retrieval procedures which
may thus be performed effectively on any SHS-structure. Due to its
procedural definition, it also allows to detect varying dependencies
of identically labeled nodes under different
aspects which might change dynamically and could therefore be employed in
conceptual, pre-predicative, and semantic inferencing as opposed
to propositional, predicative, and logic deduction.
For this purpose a procedure was designed to
operate simultaniously on two (or more) DDS-trees by way of (simulated)
parallel processing. The algorithm is started by two (or more) meaning
points which may be considered to represent conceptual premises.
Their DDS can be generated while the actual inferencing procedure
begins to work its way (breadth-first, depth-first, or
according to highest criteriality) through both (or more) trees,
tagging each encountered node. When the first node is
met that has previously been tagged by activation from another premise,
the search procedure stops to activate the dependency paths
from this concluding common node back to the premises,
listing the intermediate nodes to mediate (as illustrated in Tab.
3) the semantic inference paths as part of the
dispositional dependencies structures DDS concerned.
It is hoped that our system will prove to provide a flexible,
source-oriented, contents-driven method for the multi-perspective
induction of dynamic conceptual dependencies among stereotypically represented
concepts which-being linguistically conveyed by natural language discourse
on specified subject domains-may empirically be detected, formally be
presented, and continuously be modified in order to promote the
learning and understanding of meaning by semiotic cognitive information
processing systems in fuzzy computational semantics.
1Published in:
Japanese-German Center Berlin (Eds.): Joint Japanese-European Symposium on
Fuzzy Systems 1992 [Publications of the JGCB: Series 3 Vol. 8],
Berlin (JDZB) 1994, pp. 197-217
2Varela/Thompson/Rosch (1991)
3Procedural models denote a class of
models whose interpretation is not derived from the semantics of an
underlying theory or its representation but consists in the processes
that these procedures instantiate when implemented in the computer.
The lack of an abstract (theoretical) level of representation for these
processes (and their results) other than the notation of their underlying
procedures (in some formal language) is one of the reasons why fuzzy set
theory - Zadeh (1965), (1975), (1981) - and its derivates may
provide a representational format for computational approaches to natural
language semantics.
4By
semiosis I mean [...] an action,
or influence, which is, or involves, a
coöperation of three subjects, such as sign, its object, and
its interpretant, this tri-relative influence not being in any way
resolvable into actions between pairs. (Peirce 1906, p. 282)
5Barwise/Perry
(1983)
6Rieger/Thiopoulos (1989);
Rieger (1991a), (1991b)
7''There are ways of using signs simpler than
those in which we use the signs of our highly complicated everyday language.
Language games are the forms of language with which a child begins
to make use of words. [ ... ] We are not, however, regarding the
language games which we describe as incomplete parts of a language, but as
languages complete in themselves, as complete systems of human
communication.'' (Wittgenstein 1958, pp. 17 and 81; [my italics ])
8"[...]
feedback is a method of controlling a system by
reinserting into it the results of its past performance. If
these results are merely used as numerical data for the
criticism of the system and its regulations, we have the
simple feedback of control engineers. If, however, the
information which proceeds backward from the performance is
able to change the general method and pattern of perfomance,
we have a process which may well be called
learning." (Wiener 1958, p. 60)
9Winograd (1986)
10Rieger 1985a
11Wittgenstein (1969)
12Rieger (1989)
13This corroborates and extends ideas expressed
within the theories of priming and spreading activation
(Lorch 1982) allowing for the dynamic generation of paths
(along which activation might spread) being a function of priming instead
of its presupposed condition.
14Prim (1957)
15Rieger (1985b)
16Rieger (1990)
17Randomly assembled from first
two pages of the daily die welt, Jg.1964, Berlin edition.
18Rieger (1984)
19Rieger (1991a), (1991b)
20"[...] an outopoetic organization constitutes
a closed domain of
relations specified with respect to the autopoetic organization
that these relations constitute, and thus it defines a space in
which it can be realized as a concrete system, a space whose dimensions
are the relations of production of the components that realize
it." (Maturana/Varela 1980, p. 135)
21Autopoietic principles of such a semiotic system
were modelled also as mathematical topoi and got implemented
successfully within a dynamic interpreter for PROLOG facts by
C. Thiopoulos in his PhD-thesis (1991), completed at the Deptartment of
Computational Linguistics, University of Trier.
3.4
Sofar the system of word meanings (lexical knowledge) has been
represented as a relational
data structure whose linguistically labeled elements (meaning points)
and their mutual distances (meaning differences) form a system of
potential stereotypes. Although theses representations by labeld
points appears to be symbolic it is worth mentioning that in fact
each such point is determined by a fuzzy distribution of
wordtype-value-pairs which allows to be interpreted as a point in SHS
whose very position is analogous to its symbolic meaning.
Accordingly, based upon SHS-structure, the meaning of a lexical
item may be described either as a fuzzy subset of the vocabulary, or as a
meaning point vector, or as a meaning point's topological environment. The
latter is determined by those points which are found to be most adjacent and
hence will delimit the central point's meaning indirectly as its
stereotype (Tab. ).
3.5
Other than in pre-defined semantic networks and predicative knowledge
bases, and unlike conceptual representations that link nodes to one
another according to what cognitive scientists believe to know or
supposedly have found out about the way conceptual information is structured
in memory, an algorithm has
been devised which operates on the SHS-data to induce dispositional
dependencies between its elements, i.e. among subsets of
meaning points related by their position. The procedure
detects fragments from SHS according to different perspectives as
specified by the meaning point it is started with, and it (re-)organizes
relevant meaning points according to the constraints of semantic
similarity encountered during operation. Stop-conditions may deliberately
be formulated either qualitatively (i.e. naming a target point) or
quantitatively (i.e. number of points, realm of distance or criteriality to
be processed).
4 The need for SCIPS
From the communicative point-of-view natural language
texts, whether stored electronically or written conventionally,
will in the foreseeable future provide the major source
of scientifically, historically, and socially relevant information.
Due to the new technologies, the amount of such textual
information continues to grow beyond manageable quantities. Rapid
access and availability of data, therefore, no longer serves
to solve an assumed problem of lack of information
to fill an obvious knowledge gap in a given instance, but is
instead and will even more so in future create a new problem which
arises from the abundance of information we are confronted with.
4.1
Development of earlier proposals18,
resulted in some promising advances19
towards an artificial semiotic cognitive information processing system
(SCIPS) which is capable of learning to understand (identify and
interpret) the meanings in natural language texts by generating dynamic
conceptual dependencies (for inferencing).
4.2
In view of a text skimming system under development22,
a basic cognitive algorithm will detect
from the textual environment the system is exposed
to, those strucural information which the system is able to collect
due to the two-level structure of its linguistic information processing and
knowledge acquisition mechanisms. These allow for the
automatic generation of a pre-predicative and formal
representation of fuzzy lexical knowledge which the system
will both, gather from and modify according to the input texts processed.
The system's internal knowledge representation will be made
accessible by a front-end which allows
system-users to make the system skim masses of texts for them and
display its acquired knowledge graphically in dynamic structures of
semantic dispositional dependencies (DDS). These provide variable
constraints for the procedural modelling of conceptual connectedness
and non-propositional inferencing which both are based on the algorithmic
induction of an aspect-dependent relevance relation connecting lexical
meanings according to differing conceptual perspektives.
Thus, the display of DDS s or their resultant graphs may
serve the user to acquire an overall idea of what the texts processed
are roughly about, or along what general lines of conceptual
dependencies they deal with a topic. DDSs may as well be employed in
an knowledge processing environment to provide the user with relevant
new keywords for an
optimized recall-precision ratio in intelligent retrieval tasks,
helping for instance to avoid unnecessary reading of texts, irrelevant
to topics the searcher is looking for.
References
Footnotes: