Zugl.: Ljubljana, Univ., Diss., 2008

Denna slutreflektion beskriver mitt arbete med mitt kandidatarbete samt mina tankegånger under och efter projektet samt hur jag arbetat, de problem jag stött på och hur jag löst dem. Delarna av denna reflektion är först en beskrivning av vad jag gjort, därefter en beskrivning av hur detta projekt var tänkt att fungera. Den tredje delen är en beskrivning av hur jag arbetat under projektet, baserat på mina veckorapporter. Den fjärde delen är själva reflektionen och den beskriver mitt arbete i mer detalj samt mina tankar och funderingar och hur jag löst de problem som uppstått. Den sista delen är ett slutord där jag sammanfattar mina tankar om utbildningen och mitt projekt jämfört med de liknande spel som finns idag samt mina tankar kring genren i allmänhet. / • Detta är en reflektionsdel till en digital medieproduktion.

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This research project aims to provide a clear and concise guide to latent dirichlet allocation which is a form of topic modelling. The aim is to help researchers who do not have a strong background in mathematics or statistics to feel comfortable with using topic modelling in their work. In order to achieve this, the thesis provides a step-by-step explanation of how topic modelling works. A range of tools that can be used to perform a topic model analysis are also described. The first chapter gives an explanation of how topic modelling, and (more specifically), latent dirichlet allocation works; it offers a very basic explanation and then provides an easy to follow mathematical explanation. The second chapter explains how to perform a topic model analysis; this is done through an explanation of each step used to run a topic model analysis, starting from the type of dataset through to the software packages available to use. The third section provides an example topic model analysis, based on the Philpapers dataset. The final section provides a discussion on the highlights of each chapter and areas for further research.

Flannery O'Connor makes the invisible visible. Just as a speaker of sign language punctuates her narrative with signs that are at once pictures and words, O'Connor punctuates the narratives of her novels with moments or pauses in the forward motion of her text that are somehow framed--in a mirror, or in a window, for example--and that also are at once pictures and words. These pictorial moments not only occur in the reader's present, but because of the way they are stylized, they are simultaneously: open windows into the historical world of the mid-twentieth century; they look backward into the classical past; and they offer a veiled look into the mystery of a Divine reality. Examination of the chronological development and refinement of Flannery O'Connor's pictorial technique by considering the meaning conveyed by the arrangement of figures in a single panel cartoon, the contextual significance found in literary tableaux and filmic montage, the use of the pictorial "camera eye," and the imprinting of tattoo on the human body, presents a new perspective in interpreting her work. Early manifestation of the pictorial technique is evident in O'Connor's college cartoons. When that cartoonist becomes a novelist that tendency for exaggeration is evident in his or her pictorial renditions of characters and situations, as is the case with former cartoonists Faulkner, Updike, West, Cantor, and O'Connor herself. O'Connor does not abandon the power of the pictorial in delivering a message. Instead she embraces it and envelops it in narrative.

The theme of the present qualification thesis for the degree of Master is “Specifics of generation of the text with real and fictitious reference with regard to age aspect”. We have examined the text from the psycholinguistic point of view and analyzed mechanisms of generation of the text with real and fictitious reference. Most attention was paid to the following issues: 5) Are there any speech tools that enable recognition of one or another type of text containing real and fictitious reference; 6) At what extent does the audience recognize such tools? After all, the thesis is aimed at solving the issue of recognizing lie on the speech level. We have studied theoretical works of P. Ackman, V. Znakov and have come to the conclusion that never before psycholinguists have taken as object of regard speech behavior of the recipients, who are trying to recognize lie in a text. In the thesis, we have tried to analyze texts with real and fictitious reference reviewed from the recipient’s point of view. Our thesis has been done in the framework of psycholinguistics headed by Prof. V.P. Belyanin.

This thesis analyzes reference in two types of context-linguistic and non-linguistic. The research focuses on systemizing and analyzing reference devices by indefinite and definite noun phrases.

Proteins play many roles in the body, and the task of understanding how proteins function is very challenging. Determining a protein’s location within the cell (also referred to as the subcellular location) helps shed light on the function of that protein. Protein subcellular location can be inferred through experimental methods or predicted using computational systems. In particular, we focus on two existing computational systems, namely EpiLoc and HomoLoc, that use features derived from text (abstracts of technical papers), and apply a support vector machine (SVM) classifier to classify proteins into their respective locations. Both EpiLoc and HomoLoc’s prediction accuracy is comparable to that of state-of-the-art protein location prediction systems. However, in addition to accuracy, other factors such as training efficiency must be considered in evaluating the quality of a location prediction system. In this thesis, we replace the SVM classifier in EpiLoc and HomoLoc, by a naïve Bayes classifier and by a novel classifier which we call the Mean Weight Text classifier. The Mean Weight Text classifier and the naïve Bayes classifier are simple to implement and execute efficiently. In addition, naïve Bayes classifiers have been shown effective in the context of protein location prediction and are considered preferable to SVM due to clarity in explaining the process used to derive the results. Evaluating the performance of these classifiers on existing data sets, we find that SVM classifiers have a slightly higher accuracy than naïve Bayes and Mean Weight Text classifiers. This slight advantage is offset by the simplicity and efficiency offered by naïve Bayes and Mean Weight Text classifiers. Moreover, we find that the Mean Weight Text classifier has a slightly higher accuracy than the naïve Bayes classifier. / Thesis (Master, Computing) -- Queen's University, 2010-07-06 11:06:47.613

Session ID: TH-B1-3 / SCIS & ISIS 2010, Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems. December 8-12, 2010, Okayama Convention Center, Okayama, Japan

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