Janos J. Sarbo

Natural language concept analysis

Can we do text analysis without phrase structure? We think the answer could be positive. In this paper we outline the basics of an underlying theory which yields hierarchical structure as the result of more abstract principles relating to the combinatorial properties of linguistic units. We discuss the nature of these properties and illustrate our model with examples.

A Peircean Ontology of Language

Formal models of natural language often suffer from excessive complexity. A reason for this, we think, may be due to the underlying approach itself. In this paper we introduce a novel, semiotic based model of language which provides us with a simple algorithm for language processing.

A Linearly Complex Model for Knowledge Representation

We present two results which complete our Peircean semiotic model of signs introduced in [10]. The first result is concerned with the potential of our model for the representation of knowledge. The second one consists of a formal proof about the models complexity.

A Peircean Framework of Syntactic Structure

A semiotic framework for the syntactic structure of language is introduced. From properties of syntactic signs a parsing algorithm is derived. Using English as aji example it is shown that, by means of its syntactic structures, the English language implements signs, analogous to those of Peirces semiotic triads.

A Peircean Ontology of Semantics

Peirces semiotics can be effectively used for modeling different sorts of signs. In this paper it is argued that semantic signs, which are signs from the semantic point of view, are no exception. It turns out, however, that a proper modeling of semantic signs needs a better understanding of the concept of qualisigns, as well as, of the relation between Peirces categories and his theory of signs.

KNOWLEDGE in FORMATION

The aim of this project is the development of a cognitively based, semiotic model of human information processing with applications in ‘naive’ logical and mathematical symbol processing, natural language parsing, meaningful summarization, ontology design and problem elicitation.

Peircean Proto‐Signs

Human knowledge is intentional, as opposed to ‘knowledge’ represented by the computer, which is syntactic. The premise of this paper is that nevertheless a process model of cognition can be defined which is isomorphic and analogous to Peirce’s 9‐adic classification of signs. An advantage of the relation with the Peircean concepts lies in the model’s potential for the definition of a ‘natural’ representation of knowledge, a representation which can be more easily interpreted than the traditional formal ones.

Building Sub-Knowledge Bases Using Concept Lattices

A theory of concept (Galois) lattices was first introduced by Wille. An extension of his work to simple structures called concept sublattices has also been published. This paper shows that concept sublattices can be applied to (i) determining subsumption of specifications and (ii) decomposing specifications in terms of others. I show that the latter application of the theory may provide us with new conceptualizations of a specification.

Grammar transformations for optimizing backtrack parsers

Abstract We present two grammar transformations which can decrease the search space of generated top-down backtrack parsers. The transformations are simple and can be of practical use. The first transformation, which is a combination of substitution and left-factorization, is based on the LR-table construction. The second transformation uses the calculation of the sets FIRST and FOLLOW, and a grammar property, called rrelative unambiguity. The time complexity of the transformations is worst case polynomial and in practical cases linear in the size of the grammar.

Formal conceptual structure in language

Conceptual structures like conceptual graphs and concept lattices are formal representations of knowledge, e.g. structure or information content. In this paper, it is argued that, based on Peirce’s semeiotic theory of signs and linguistic knowledge about syntactic clusters, conceptual structures can be derived in natural language automatically. This is incorporated in a novel model of language called Natural Language Concept Analysis (NLCA).