Procedural Meaning Representation By Connotative Dependency Structures. An Empirical Approach To Word Semantics For Analogical Referencing

The c o n c e p t o f ' r e p r e s e n t a t i o n o f k n o w l e d g e ' seems l u c i d e n o u g h when talking about memories of sentences, numbers,or even faces, for one can imagine how to formulate these in terms of propositions, frames, or semantic networks. But it is much harder to do this for feelings, insights and understandings, with all the attitudes, dispositions, and 'ways of seeing things' that go with them. [The term 'disposition' is used here in its ordinary language sense to mean 'a momentary range of possible behaviours']) Traditionally, such issues are put aside, with the excuse that we should understand simpler things first. But what if feelings and viewpoints a r e the simpler things the elements of which the others are composed? Then, I assert, we should deal with dispositions directly, using a 'structural' approach ... (2) In the present case this has been developed in two stages: the semantic space as a distance-like data structure, and an algorithm to transform its distance relations to form source-oriented hierarchies of connotative dependency structures. SEMANTIC SPACE STRUCTURE Theoretical approaches in formal semantics tend to deny a dynamic linguistic meaning structure, but assume the existence of an external system structure of a world, o~ possible worlds, whose pre-formatted entities may referentially be related to language terms constituting their denotation. Structural approaches in linguistic semantics tend to deny the possibility of denotational, but presuppose the knowledge (and comprehension) of language systems whose semantic relations among their items are being described intra-lingually by means of syntagmatic and paradigmatic oppositions along certain dimensions in semantic fields. Other than these two, our present way of approach strives to presuppose as little and to reconstruct empirically as much as possible of the relational (not necessarily logically reconstructable) structure that in the course of discourse is constituted by the regular use of language terms as a system of linguistically labeled empirical objects, called meanings. We consider the natural language users' ability to intend and comprehend meanings in verbal interaction a phenomenologically undoubtable, empirically well established, and theoretically defensible basis for any semantic study of natural language performance. It is assumed that the usage regularities followed and/or established by employing different lexical items differently for communicative purposes in discourse may be analysed not only to describe the lexical structure of vocabulary items used, but also to model a fragment of the conEMPIRICAL APPROACH TO WORD SEMANTICS FOR ANALOGICAL INFERENCING 321 comitantly conveyed common knowledge or semantic memory structure constituted. This is achieved by an algorithm that takes lemmatized strings of natural language disc6urse of a certain domain as input and produces as output a distance-like data structure of linguistically labeled points whose positions represent their meanings. As the statistical means for the empirical analysis of prevailing interdependencies between lexical items in text strings have elsewhere (3) been developed and discussed to some extent (4) 9 and as the formal representation of vague word meanings derived from these analyses has previously (5) been outlined and illustrated~ too (6), an informal description will suffice here. The algorithm applied so far consists of a consecutive mapping of lexical items onto fuzzy subsets of the vocabulary according to the numerically specified statistical regularities and the differences~ these items have been used with in the discourse analysed. The resulting system of sets of fuzzy subsets may be interpreted topologically as a n-dimensional hyperspace with a natural metric. Its n linguistically labeled elements (representing meaning points) and their mutual distances (representing meaning differences) form discernable clouds and clusters (7). These determine the overall structuredness of a domain by measurable semantic (paradigmatic and/or syntagmatic) properties of the lexical items concerned. CONNOTATIVE DEPENDENCY STRUCTURE Stimulated by the theory of spreading activation in memory models (8) in conjunction with the psyhhological account of language understanding in procedural semantics (9) a dynamic meaning representation can be developed of the basis of the prototypical, but static representations provided by the semantic hyperspace strucure. This is mchieved by a recursively defined algorithm which has formally been introduced elsewhere (Io) so that it may verbally be described here as a procedure to generate a potential of latent relations among meaning points in the semantic space. In a way9 this procedure reconstructs for this model what recent theories of cognition and language comprehension have introduced in network models of semantic memory: paths of excitation that may be activated from any primed node and which spread along node relating links over the whole network with decreasing intensities. Compared to the execution of spreading activation processes in network models~ however, the present procedure speaking in model genetical terms must be considered of prior status. The semantic hyperspace is not a transitively related network of nodes, but a symmetrically related data structure of linguistically labeled n-tuples of numerical values. Therefore, priming of any item would immediately activate every other item rendering the process of spreading activation undiscriminating for semantic representation. So, the new procedure, first, has to establish links between items and evaluate them by processing the data base provided in order to let these links eventually serve as directed paths along which possible activation might spread. Operating on the distance-like data of the semantic space, the algorithm's generic procedure will start with any meaning point being primed to determine those two other points, the sum of distances between which form a triangle of minimum edges' lengths. Repeated successively for each of these meaning points listed and in turn primed in accordance with this procedure, particular fragments of the relational structure inherent in the semantic space will be selected depending on the aspect, i.e. the primed point the algorithm is initially started with. Working its way through and consuming all labeled points in the space system, the procedure transforms prevailing similarities of