The question of whether a machine can reproduce human intelligence is older than modern computation, but has received a great deal of attention since the first digital computers emerged decades ago. Language understanding, a hallmark of human intelligence, has been the focus of a great deal of work in Artificial Intelligence (AI). In 1950, mathematician Alan Turing proposed a kind of game, or test, to evaluate the intelligence of a machine by assessing its ability to understand written natural language. But nearly sixty years after Turing proposed his test of machine intelligence—pose questions to a machine and a person without seeing either, and try to determine which is the machine—no system has passed the Turing Test, and the question of whether a machine can understand natural language cannot yet be answered. The present investigation is, firstly, an attempt to advance the state of the art in natural language understanding by building a machine whose input is English natural language and whose output is a set of assertions that represent answers to certain questions posed about the content of the input. The machine we explore here, in other words, should pass a simplified version of the Turing Test and by doing so help clarify and expand on our understanding of the machine intelligence. Toward this goal, we explore a constraint framework for partial solutions to the Turing Test, propose a problem whose solution would constitute a significant advance in natural language processing, and design and implement a system adequate for addressing the problem proposed. The fully implemented system finds primary specific events and their locations in monologue discourse using a hierarchical classification approach, and as such provides answers to questions of central importance in the interpretation of discourse. / text

(in English): The diploma thesis explores the means of expressing interpersonal function (metadiscourse) in English academic lectures. This function includes means mitigating the proposition of authors (hedges), expressions boosting its credibility (booster), instances reflecting attitude of the author (attitude markers) and means referring to both, the author himself (self-mentions) and the audience (engagement markers). For the purpose of the analysis, the integrative approach of Ken Hyland was chosen. It explores interpersonal resources as well as interactive resources in written academic discourse. Means organizing text into an intelligible and comprehensible unit will be also studied. The excerpted instances of metadiscourse were examined with respect to their function and realization form. In a case of realization forms, we expected to find means expressing modality (modal verbs, adverbs, adjectives), evaluative adjectives and adverbs, conjunctions and an array of personal pronouns referring to the participants of lectures. The findings showed surprising deviations in the categories of boosters, extended frame markers and attitude markers. Remaining categories, despite the different mode of the data (spoken academic language) corresponded with Hyland's findings from written academic discourse.