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Some Notes on Extracting Linguistic Summaries Built with Epistemic Modalities and Natural Language Connectives of Equivalence

Katarzyniak R., Lorkiewicz W., Więcek D.

Computational Methods in Science and Technology

2017

Vol. 23, No. 1

19--41

In this paper we deal with an original technically oriented model for cognitive semantics. As the expected area of application we focus on the process of extraction of modal linguistic summaries from data managed by autonomous components of ambient systems and intelligent environments. As such, the cognitive semantics is defined for a particular case of modal natural language statements with epistemic modalities. The statements of interest are built with natural language operators, representing epistemic modalities (related to the main cognitive states of knowledge certainty: full certainty, strong belief and epistemic possibility), and natural language connectives of equivalence. Furthermore, an approach to their effective processing by autonomous computational systems is designed. An internal architecture of the autonomous computational component is designed with respect to modular model for natural language processing with separate modules for epistemic and semantic memory storage and processing. An original theoretical concept underlying the model of semantic memory is a holon defined as a collection of complementary linguistic protoforms. Finally, we provide several illustrative computational examples of linguistic summaries’ extraction, based on artificial and real data.

Invariant Levenstein Distance as an example of the Hausdorff Distance

Bartyzel K.

Journal of Applied Computer Science

2014

Vol. 22, nr 2

7--17

The properties and applications of chain codes are studied around the world for many years. One of the most important uses is the ability to use them (chain codes) to describe objects contours and therefore for the comparison of objects. In previous work author presented Levenstein distance modification which allows to compare Brownian strings. In this paper, author focuses on expanding studies on developed distance and confirmation that the obtained measure is truly the mathematical metric.

Invariant Levenstein Distance for Comparison of Brownian Strings

Bartyzel K.

Journal of Applied Computer Science

2010

Vol. 18, nr 1

7-17

The paper describes a new way of comparing Brownian contours. The Brownian strings, one of the main chain symbolic structures, are an image segmentation technique where a stochastically deformable contour fits around an object. In this technique the chain code can be interpreted as a simple method for linguistic description of the object. The Levenstein distance, which is one of the known textual strings comparison method, was generalised in a way that it is able to become insensitive to rotation and mirror reflection of compared shapes.