Constructing an encoding of a concept lattice using short bit vectors allows for efﬁcient computation of join operations on the lattice. Join is the central operation any uniﬁcation-based parser must support. We extend the traditional bit vector encoding, which represents join failure using the zero vector, to count any vector with less than a ﬁxed number of one bits as failure.
In this paper, we introduce a new encoding for a given binary relation, by using adjacency matrix constructed on the relation. Therefore, a coatom of a concept lattice can be characterized by supports of row vectors of adjacency matrix. Moreover, we are able to compute a poly-sized sub-relation resulting in a sublattice of the original lattice for a given binary relation.
Database Systems: Lecture 3 - EER Model presents about Limitations of Basic Concepts of the ER Model, Enhanced-ER (EER) Model Concepts, Subclasses and Superclasses, Specialization and Generalization, Specialization / Generalization Hierarchies, Lattices and Shared Subclasses.
This chapter provides a brief introduction to the theory of morphological signal processing and its
applications toimage analysis andnonlinear filtering. By “morphological signal processing”we mean
a broad and coherent collection of theoretical concepts, mathematical tools for signal analysis, nonlinear
signal operators, design methodologies, and applications systems that are based on or related
to mathematical morphology (MM), a set- and lattice-theoreticmethodology for image analysis. MM
aims at quantitatively describing the geometrical structure of image objects.
This book addresses the engineering student and practising engineer. It
takes an engineering-oriented look at semiconductors. Semiconductors
are at the focal point of vast number of technologists, resulting in great
engineering, amazing products and unheard-of capital growth. The work
horse here is of course silicon. Explaining how semiconductors like silicon
behave, and how they can be manipulated to make microchips that
work—this is the goal of our book.
A language processor is to find out a most promising
sentence hypothesis for a given word lattice obtained
from acoustic signal recognition. In this paper a new language processor is proposed, in which unification granunar and Markov language model are integrated in a word lattice parsing algorithm based on an augmented chart, and the island-driven parsing concept is combined with various preference-first parsing strategies defined by different construction principles and decision rules.
Languages differ in the concepts and real-world entities for which they have words and grammatical constructs. Therefore translation must sometimes be a m a t t e r of approximating the meaning of a source language text rather than finding an exact counterpart in the target language. We propose a translation framework based on Situation Theory. The basic ingredients are an information lattice, a representation scheme for utterances embedded in contexts, and a mismatch resolution scheme defined in terms of information flow.