Burghard B. Rieger

Semiotic Cognitive Information Processing: Learning to Understand Discourse. A Systemic Model of Meaning Constitution

Human beings appear to be very particular information processing systems whose outstanding plasticity and capability to cope with changing environmental conditions (adaptation) is essentially tied to their use of natural languages in communication to acquire knowledge (learning). Their knowledge based processing of information makes them cognitive,and their sign and symbol generation, manipulation, and understanding capabilities render them semiotic. Semiotic cognitive information processing (SCIP) is inspired by information systems theory according to which living systems process and structure environmental data according to their own structuredness. When these processes are modeled as operating on structures whose representational status is not so much a presupposition to but rather a result from such processing, then the resulting models — being able to simultaneously instanciate, create and/or modify these structures — may attain a quality of sign and symbol understanding which may computationally be realized. This quality will in the sequel be studied and identified as a particular form of knowledge acquisition or learning whose results can be visualized as incremental dynamics of structure formation. Its formal delineation, operational specification, and algorithmic implementation allows for experimental testing of the SCIP system’s capability for meaning constitution from natural language texts without prior morphological, lexical, syntactic and/or semantic knowledge.

SEMANTIC RELEVANCE AND ASPECT DEPENDENCY IN A GIVEN SUBJECT DOMAIN Contents-driven algorithmic processing of fuzzy wordmeanings to form dynamic stereotype representations

Cognitive principles underlying the (re-) construction of word meaning and/or world knowledge structures are poorly understood yet. In a rather sharp departure from more orthodox lines of introspective acquisition of structural data on meaning and knowledge representation in cognitive science, an empirical approach is explored that analyses natural language data statistically, represents its numerical findings fuzzy-set theoretically, and interprets its intermediate constructs (stereotype meaning points) topologically as elements of semantic space. As connotative meaning representations, these elements allow an aspect-controlled, contents-driven algorithm to operate which reorganizes them dynamically in dispositional dependency structures (DDS-trees) which constitute a procedurally defined meaning representation format.

Semiotics and Computational Linguistics

Signs, which are the domain of inquiry in semiotics, have a complex ontology. Apart from being used—adequate knowledge provided—by communicators, and recognized as being decomposable into smaller elements and aggregatable to larger structures, they are also meant to be understood. This is a consequence of their manifold identity as compound physical objects with real world extensions in space-time-locations and as activators for complex mental processes which tend to be identified with some mind and/or brain activities responsible for their understanding. In the cognitive sciences all processes of perception, identification, and interpretation of (external) structures are considered information processing which (natural or artificial) systems—due to their own (internal) structuredness or knowledge—are able (or unable) to perform. Combining the semiotic with the cognitive paradigm in computational linguistics, the processes believed to constitute natural language sign structures and their understanding is modeled by way of procedural, i.e. computational (re-)constructions of such processes that produce structures comparable to those that the understanding of (very large) samples of situated natural language discourse would imply. Thus, computational semiotic models in cognitive linguistics aim at simulating the constitution of meanings and the interpretation of signs without their predicative and propositional representations which dominate traditional research formats in syntax and semantics so far. This is achieved by analyzing the linear or syntagmatic and selective or paradigmatic constraints which natural languages impose recursively on the formation and structure of (strings of) linguistic entities on different levels of systemic distinction. It will be argued (and illustrated) that fuzzy modeling allows to derive more adequate representational means whose (numerical) specificity and (procedural) definiteness may complement formats of categorial type precision (which would appear phenomenologically incompatible) and processual determinateness (which would seem cognitively inadequate). Several examples from fuzzy linguistic research will be given to illustrate these points.

Meaning acquisition by SCIPS

The emergence of semantic structure as a self-organizing process is studied in semiotic cognitive information processing systems on the basis of word usage regularities in natural language discourse whose linearly agglomerative (syntagmatic) and whose selectively interchangeable (paradigmatic) constraints are exploited by text analysing algorithms. They accept natural language discourse as input and produce a vector space structure as output which may be interpreted as an internal (endo) representation of the SCIP systems states of adaptation to the external (exo) structures of its environment as mediated by the discourse processed. In order to evaluate the sytems endo-representation against the exo-view of its environment as described by the natural language discourse processed, a corpus of texts-composed of correct and true sentences with well-defined referential meanings-was generated according to a (very simple) phrase structure grammar and a fuzzy referential semantics which interpret simple composite predicates of cores (like: on the left, in front etc.) and hedges (like: extremely nearby, very faraway etc.). Processed during the systems training phase, the corpus reveals structural constraints which the systems hidden structures or internal meaning representations apparently reflect. The systems architecture is a two-level consecutive mapping of distributed representations of systems of (fuzzy) linguistic entities whose states acquire symbolic functions that can be equaled to (basal) referencial predicates. Test results from an experimental setting with varying fuzzy interpretations of hedges are produced to illustrate the SCIP systems miniature (cognitive) language understanding and meaning acquisition capacity without any initial explicit syntactic and semantic knowledge.

A self-organizing lexical system in hypertext ⁄

Our understanding of the bunch of complex intellectual activities subsumed under the notion of cognition is still very limited, particularly in how knowledge is acquired from texts and what processes are responsible for it. Recent achievements in wordsemantics, conceptual structuring, and knowledge representation within the intersection of cognitive psychology, artificial intelligence and computational linguistics have shown some agreement though. It appears that cognition is (among others) responsible for, if not identifiable with, the processes according to which for a cognitive system previously unstructured surroundings may be tranformed to its perceived environment whose identifiable portions and their relatedness does not only constitute structures but also allow for their permanent revision according to the system’s capabilities.

Unscharfe Semantik: zur numerischen Modellierung vager Bedeutungen von Wörtern als fuzzy Mengen

Analyse und Reprasentation lexikalisch-semantischer Vagheit von Bedeutungen in naturlichsprachlichen Texten stellen ein Problem der Wortsemantik dar. Dies last sich mithilfe der Theorie der unscharfen Mengen in einer formal adaquaten, empirisch fundierbaren und prozedura- len Modellbildung losen. Nach kurzer Einfuhrung der wesentlichen Charakteristika dieses die Unscharfe einbeziehenden Modells werden seine formalen Eigenschaften zunachst anhand einer denotativ-referenziellen Bedeutungsdarstellung gegeben, deren Kritik die Bedingungen liefert, welche eine empirisch-operationale Rekonstruktion und numerische Modellierung von stereotypischen Wortbedeutungen zu erfullen hat. Fur diese wird eine formale Struktur entwickelt, in der die systematischen Restriktionen von Lexemverwendung (ihre syntagmatischen und paradigmatischen Relationen) uber eine zweistufige Abstraktion sich darstellen lassen. Sie konnen als konsekutive Abbildung der beobachtbaren Unterschiede von Verwendungsregularita- ten von Lexemen in Texten auch empirisch rekonstruieren werden, was zur algorithmischen Reprasentation von Wortbedeutungen als einem System von unscharfen Mengen des Vokabulars bzw. von Vektoren im semantischen Raum fuhrt. Dessen Topologie bildet die Grundlage dafur, unterschiedliche semantische Zusammenhange so reprasentierter Bedeutungen unter verschiedenen inhaltlichen Perspektiven algorithmisch zu generieren und als Dispositioneile De- pendenzstrukturen (DDS) organisiert zuganglich zu machen.

Situations, Topoi, and Dispositions

Submitting to the dualism of the rationalistic tradition of thought and its notions of some (objective) reality and the (subjective) conceptions of it, BARWISE/PERRY (1983) have presented a new approach to formal semantics which, essentially, can still be considered a mapping of this duality, mediated though by their notion of situation. Within their relational model of meaning, any language expression is tied to reality in two ways: by the discourse situation allowing its meaning being interpreted and by the described situation allowing its interpretation being evaluated truth-functionally. This is achieved by recognizing similarities or invariants between situations that structure a system’s surrounding environments (or fragments thereof). Mapping these invariants as uniformities across situations, cognitive systems 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 individuals, relations, and time-space-locations which constrain “a world teaming with meaning”1 to become interpretable fragments as persistent courses of events.

Procedural meaning representation by connotative dependency structures: an empirical approach to word semantics for analogical inferencing

Natural language understanding systems make use of language and/or world knowledge bases. One of the salient problems of meaning representation and knowledge structure is the modelling of its acquisition and modification from natural language processing. Based upon the statistical analysis of discourse, a formal representation of vague word meanins is derived which constitutes the lexical structure of the vocabulary employed in the texts as a fragment of the connotative knowledge conveyed in discourse. It consists of a distance-like data structure of linguistically labeled space points whose positions give a prototype-representation of conceptual meanings. On the basis of these semantic space data an algorithm is presented which transforms prevailing similarities of conceptual meanings as denoted by adjacent space points to establish a binary, non-symmetric, and transitive relation between them. This allows for the hierarchical reorganization of points as nodes dependent on a head in a binary tree called connotative dependency structure (CDS). It offers an empirically founded operational approach to determine relevant portions of the space structure constituting semantic dispositions which the priming of a meaning point will trigger with decreasing criteriality. Thus, the CDS allows for the execution of associatively guided search strategies, contents-oriented retrieval operations, and source-dependent processes of analogical inferencing.

A systems theoretical view on computational semiotics. Modeling text understanding as meaning constitution by SCIPS

In a rather sharp departure from CL and AI approaches, modeling in computational semiotics (CS) neither presupposes rule-based or symbolic formats for linguistic knowledge representations, nor does it subscribe to the notion of symbolically represented world knowledge as some static structures that may be abstracted from and formatted independently of the way they are processed. Consequently, knowledge structures and the processes operating on them are to be modeled procedurally and ought to be implemented as algorithms. They determine semiotic cognitive information processing systems (SCIPS) as collections of cognitive information processing devices whose semiotic character consists in their multilevel representational system of (working) structures emerging from and being modified by such processing. According to different types of cognitive modeling distinguished in the past, computational semiotics can be characterized as aiming at the dynamics of emergent meaning constituted by processes which may be simulated as multiresolutional representations within the frame of an ecological information processing paradigm.

On Generating Semantic Dispositions in a Given Subject Domain

Modeling system structures of word meanings and/or world knowledge is to face the problem of their mutual and complex relatedness. In linguistic semantics, cognitive psychology, and knowledge representation most of the necessary data concerning lexical, semantic and/or external world information is still provided introspectively. In a rather sharp departure from that form of data acquisition the present approach has been based on the empirical analysis of discourse that real speakers/writers produce in actual situations of performed or intended communication in prescriptive contexts or subject domains. The approach makes essential use of statistical means to analyze usage regularities of words to map their fuzzy meanings and connotative interrelations in a format of stereotypes. Their dependencies are generated algorithmically as multi-perspective dispositions that render only those relations accessible to automatic processing which can - under differing aspects differently — be considered relevant. Generating such semantic dispositional dependencies dynamically by a procedure would seem to be an operational prerequisitie to and a promising candidate for the simulation of contents-driven (analogically-associative), instead of formal (logically-deductive) inferences in semantic processing.