Learning algebraic structures from text

Learning algebraic structures from text

The present work investigates the learnability of classes of substructures of some algebraic structure: submonoids and subgroups of some given group, ideals of some given commutative ring, subbelds of a vector space. The learner sees all positive data but no negative one and converges to a program enumerating or computing the set to be learned. Besides semantical (BC) and syntactical (Ex) conve...

Automatic Text Generation by Learning from Literary Structures

Most of the work dealing with automatic story production is based on a generic architecture for text generation; however, the resulting stories still lack a style that can be called literary. We believe that in order to generate automatically stories that could be compared with those by human authors, a specific methodology for fiction text generation should be defined. We also believe that it ...

Learning theories from text

In this paper we describe a method of automatically learning domain theories from parsed corpora of sentences from the relevant domain and use FSA techniques for the graphical representation of such a theory. By a ‘domain theory’ we mean a collection of facts and generalisations or rules which capture what commonly happens (or does not happen) in some domain of interest. As language users, we i...

Learning from Random Text

Lie algebraic noncommuting structures from reparametrisation symmetry

We extend our earlier work of revealing both space-space and space-time noncommuting structures in various models in particle mechanics exhibiting reparametrisation symmetry. We show explicitly (in contrast to the earlier results in our paper [8]) that for some special choices of the reparametrisation parameter ǫ, one can obtain space-space noncommuting structures which are Lie-algebraic in for...