Burghard B. Rieger:
Inducing a Relevance Relation in a Distance-like Data Structure of Fuzzy Word Meaning Representations
In: Allen, R.F. (eds.): Data Bases in the Humanities and Social Sciences (Proceedings: 4th International Conference ICDBHSS/83), Osprey, FL (Paradigm Press) 1985, pp. 374-386
Abstract
Modelling representational systems for word meanings
and/or world knowledge is a problem of mutual and complex
relatedness. Different formats have been used with
differing success among which that of stereotypical and/or prototypical meaning and knowledge representation
appeared to be most adequate in view of how conceptual
knowledge is made use of and/or new concepts are being
conveyed. Under the notion of lexical relevance and
semantic disposition this interdependency may operationally
be clarified and empirically be reconstructed
from natural language discourse - although most approaches
to word semantics and conceptual modelling do not
address these issues. Instead, linguists and psychologists, as well
as artficial intelligence experts engaged in
word meaning and/or world knowledge representation
still provide the necessary semantic and external world
information introspectively, i.e. they are exploring (or
make testpersons explore) their own competence and memory
capacities to depict their findings in some semantic
or conceptual structures (lists, arrays, networks,
etc.).
Other than these introspective explorations, the present
approach strives to derive directly via automatic analysis
of natural language discourse (input) some basic
data (output) whose relational structure need not be defined
statically in declarative terms of logical-deductive
hierarchies but will instead be imposod procedurally
by algorithms which allow for the dynamic induction
of relevant analogical-associative dependencies to form
semantic dispositions.
By way of a sketchy overview rather than a qualifying
introduction, it will (first) be outlined according to
what principles the natural language discourse is analysed
statistically and how the data obtained is represented
formally. Constituting the semantic space model
(second), its structure is examined for specific meaning
representations, their positions, environments, and clustering
properties. Starting from the notion of priming
and spreading activation in memory as a cognitive model
for comprehension processes, we will (third) deal with
our procedural method of representing semantic dispositions by way of
inducing lexical relevance relations
within semantic space. Concluding (fourth) we shall point
to two or three problem areas connected with word meaning and
concept processing which may be tackled anew and
perhaps brought to a more adequate though still tentative solution
under an empirically founded approach to procedural semantics.
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