Cooperating distributed grammar systems with random context grammars as components

In this paper, we discuss cooperating distributed grammar systems where components are (variants of) random context grammars. We give an overview of known results and open problems, and prove some further results

Simulating the Machine Translation of Low-Resource Languages by Designing a Translator Between English and an Artificially Constructed Language

Natural language processing (NLP), or the use of computers to analyze natural language, is a field that relies heavily on syntax. It would seem intuitive that computers would thrive in this area due to their strict syntax requirements, but the syntax of natural languages leaves them unable to properly parse and generate sentences that seem normal to the average speaker. A subfield of NLP, machine translation, works mainly to computerize translation between different languages. Unfortunately, such translation is not without its weaknesses; language documentation is not created equal, and many low-resource languages—languages with relatively few kinds of documentation, most often written—are left with no way to effectively benefit from machine translation. As a step toward better translation processors for low-resource languages, this thesis examined the possibility of machine translation between high resource languages and low resource languages through an analysis of different machine learning techniques, and ultimately constructing a simple translator between English and an artificially constructed language using a context-free grammar (CFG)

Regulated Parsing

Tato práce se zabývá rozšířenými modely bezkontextových gramatik a zkoumá možnosti jejich úpravy a využití pro deterministickou syntaktickou analýzu pomocí metod hluboké syntaktické analýzy struktur, které nejsou bezkontextové. Zavádí upravený bezkontextový model LL programovaných gramatik a hlubokého zásobníkového automatu, umožňující deterministickou syntaktickou analýzu těchto struktur.This work deals with advanced models of context-free grammars and explores the possibilities of adaptation and usefulness for deterministic parsing of non-context-free sructures by deep parsing method. It introduces adapted model of context-free grammar named LL programmed grammar and adapted deep pushdown automaton that makes deterministic parsing of non-context-free structures possible.