The wealth of medical-related information available today gives rise to a multidimensional source of knowledge. Research discoveries published in prestigious venues, electronic-health records data, discharge summaries, clinical notes, etc., all represent important medical information that can assist in the medical decision-making process. The challenge that comes with accessing and using such vast and diverse sources of data stands in the ability to distil and extract reliable and relevant information. Computer-based tools that use natural language processing and machine learning techniques have proven to help address such challenges. This current work proposes automatic reliable solutions for solving tasks that can help achieve a personalized-medicine, a medical practice that brings together general medical knowledge and case-specific medical information. Phenotypic medical observations, along with data coming from test results, are not enough when assessing and treating a medical case. Genetic, life-style, background and environmental data also need to be taken into account in the medical decision process. This thesis’s goal is to prove that natural language processing and machine learning techniques represent reliable solutions for solving important medical-related problems. From the numerous research problems that need to be answered when implementing personalized medicine, the scope of this thesis is restricted to four, as follows: 1. Automatic identification of obesity-related diseases by using only textual clinical data; 2. Automatic identification of relevant abstracts of published research to be used for building systematic reviews; 3. Automatic identification of gene functions based on textual data of published medical abstracts; 4. Automatic identification and classification of important medical relations between medical concepts in clinical and technical data. This thesis investigation on finding automatic solutions for achieving a personalized medicine through information identification and extraction focused on individual specific problems that can be later linked in a puzzle-building manner. A diverse representation technique that follows a divide-and-conquer methodological approach shows to be the most reliable solution for building automatic models that solve the above mentioned tasks. The methodologies that I propose are supported by in-depth research experiments and thorough discussions and conclusions.

This work seeks to address the major texts of Slavoj Zizek using a reading methodology which treats political philosophy as a practice, rather than a series of logical propositions or claims of truth or falsity. Philosophy is herein understood as a field of relations among authors who occupy precise theoretical and political coordinates. Writing produces and reproduces an author's position within this field via the way in which an author communicates with his/her peers, draws on past concepts, and designs new ones. This paper argues that Zizek cannot usefully be grasped as a theorist attempting to provide positive political solutions or analyses, but rather as a 'negative' force who occupies an impossible position by attempting to negate his peers and popular contemporary theoretical concepts - Zizek wishes to create a new intellectual space where political possibilities can be rethought and rediscovered, and he does this in his texts by ephemerally occupying multiple positions only to displace them.

This thesis explores text-music relationships in Dallapiccola’s Goethe Lieder. Though the cycle is based on Goethe’s West-östlicher divan, it was Mann’s novel Joseph und seine Brüder that spurred its inception. This seven-song cycle revolves around Suleika, a character from the biblical love story of Joseph and Potiphar’s Wife. Dallapiccola set this text upon reading Mann’s novel, which stems from the same story; however, Mann portrayed the character of Suleika as a sympathetic lover rather than the traditional evil seductress. By conducting a thorough pitch structure analysis of each song, focusing in particular on motives, symmetry and aggregates, this thesis examines text-music relationships to demonstrate how Mann’s Suleika is musically represented. This thesis illustrates that Dallapiccola’s setting is a musical composite of both Goethe and Mann’s Suleikas and thus sheds new analytical and hermeneutic light on an important work by one of the twentieth-century’s most prominent serial composers.

This thesis achieved the goal of obtaining high accuracy rates (precision and recall) in a real-time system that detects and recognizes text in the automotive context. For the sake of simplicity, this work targets two Objects of Interest (OOIs): North American (NA) traffic boards (TBs) and license plates (LPs). The proposed approach adopts a hybrid detection module consisting of a Connected Component Analysis (CCA) step followed by a Texture Analysis (TA) step. An initial set of candidates is extracted by highlighting the Maximally Stable Extremal Regions (MSERs). Each sebsequent step in the CCA and TA steps attempts to reduce the size of the set by filtering out false positives and retaining the true positives. The final set of candidates is fed into a recognition stage that integrates an open source Optical Character Reader (OCR) into the framework by using two additional steps that serve the purpose of minimizing false readings as well as the incurred delays. A set of of manually taken videos from various regions of Ottawa were used to evaluate the performance of the system, using precision, recall and latency as metrics. The high precision and recall values reflect the proposed approach's ability in removing false positives and retaining the true positives, respectively, while the low latency values deem it suitable for the automotive context. Moreover, the ability to detect two OOIs of varying appearances demonstrates the flexibility that is featured by the hybrid detection module.

According to the World Health Organization, mental health is an integral part of health and well-being. Mental illness can affect anyone, rich or poor, male or female. One such example of mental illness is depression. In Canada 5.3% of the population had presented a depressive episode in the past 12 months. Depression is difficult to diagnose, resulting in high under-diagnosis. Diagnosing depression is often based on self-reported experiences, behaviors reported by relatives, and a mental status examination. Currently, author- ities use surveys and questionnaires to identify individuals who may be at risk of depression. This process is time-consuming and costly. We propose an automated system that can identify at-risk users from their public social media activity. More specifically, we identify at-risk users from Twitter. To achieve this goal we trained a user-level classifier using Support Vector Machine (SVM) that can detect at-risk users with a recall of 0.8750 and a precision of 0.7778. We also trained a tweet-level classifier that predicts if a tweet indicates distress. This task was much more difficult due to the imbalanced data. In the dataset that we labeled, we came across 5% distress tweets and 95% non-distress tweets. To handle this class imbalance, we used undersampling methods. The resulting classifier uses SVM and performs with a recall of 0.8020 and a precision of 0.1237. Our system can be used by authorities to find a focused group of at-risk users. It is not a platform for labeling an individual as a patient with depres- sion, but only a platform for raising an alarm so that the relevant authorities could take necessary interventions to further analyze the predicted user to confirm his/her state of mental health. We respect the ethical boundaries relating to the use of social media data and therefore do not use any user identification information in our research.

Reading comprehension among boys is something that has deteriorated over the years. But is it so that boys do not read or do they just read things that does not count? Today there is an expanded textual concept. This means that even movies, comics, games, music and images counts as text, not just printed words on paper. I therefore want to investigate how boys in upper secondary school describes the text they encounter during their free time as well as the texts they encounter in school during the subject of Swedish. I want to see how their attitude is to these two textworlds. Six interviews with six diffrent boys from a upper secondary school north of Stockholm has been made. They are all in the alignment electricity program and are now in the second grade. They have all been reading the course “Svenska 1, 100p”. At first, the respondents said they did not read or meet text during their free time. They pointed out that they did not read books. When the respondents then learned that text could be more than only books, their answers changed and a textworld was created. The respondents describe the textworld in their free time as primarily screen-based. This is where they create a community with their friends and the texts has also the function to pass time. In the subject “Svenska 1” the respondents describe the textworld as non-screen based. They mean that they mainly read books. On the other hand, they see this as a positive thing as they are confident that the teacher chooses the "right" books and that they will learn something from it. This result shows that boys are reading, but usually not books. In the texts they encounter, one finds mostly male main characters. They are often supernaturally strong and smart and are classified as superheroes.

Epidemiological studies are rich in information that could improve the understanding of concept complexity of a health problem, and are important sources for evidence based medicine. However, epidemiologists experience difficulties in recognising and aggregating key characteristics in related research due to an increasing number of published articles. The main aim of this dissertation is to explore how text mining techniques can assist epidemiologists to identify important pieces of information and detect and integrate key knowledge for further research and exploration via concept maps. Concept maps are widely used in medicine for exploration and representation as a relatively formal, easy to design and understand knowledge representation model. To support this aim, we have developed a methodology for the extraction of key epidemiological characteristics from all types of epidemiological research articles in order to visualise, explore and aggregate concepts related to a health care problem. A generic rule-based approach was designed and implemented for the identification of mentions of six key characteristics, including study design, population, exposure, outcome, covariate and effect size. The system also relies on automatic term recognition and biomedical dictionaries to identify concepts of interests. In order to facilitate knowledge integration and aggregation, extracted characteristics are further normalized and mapped to existing resources. Study design mentions are mapped to an expanded version of the Ontology of Clinical Research (OCRe), whereas exposure, outcome and covariate mentions are mapped to Unified Medical Language System (UMLS) semantic groups and categories. Population mentions are mapped to age groups, gender and nationality/ethnicity, and effect size mentions are normalised with the regards to the used metric and confidence interval and related concept. The evaluation has shown reliable results, with an average micro F-score of 87% for recognition of epidemiological mentions and 91% for normalisation. Normalised concepts are further organised in an automatically generated concept map, which has three sections for exposures, outcomes and covariates. To demonstrate the potential of the developed methodology, it was applied to a large-scale corpus of epidemiological research abstracts related to obesity. Obesity was chosen as a case study since it has emerged as one of the most important global health problems of the 21st century. Using the concepts extracted from the corpus, we have built a searchable database of key epidemiological characteristics explored in obesity and an automatically generated concept map represented the normalized exposures, outcomes and covariates. An epidemiological workbench (EpiTeM) was designed to enable further exploration and inspection of the normalized extracted data, with direct links to the literature. The generated results also allow exploration of trends in obesity research and can facilitate understanding of its concept complexity. For example, we have noted the most frequent concepts and the most common pairs of characteristics that have been studied in obesity epidemiology. Finally, this thesis also discusses a number of challenges for text mining of epidemiological literature and suggests various opportunities for future work.

Purpose. This work addresses an escalating problem within the realm of scientific publishing, that stems from accelerated publication rates of article formats difficult to process automatically. The amount of manual labour required to organise a comprehensive corpus of relevant literature has long been impractical. This has, in effect, reduced research efficiency and delayed scientific advancement. Two complementary approaches meant to alleviate this problem are detailed and improved upon beyond the current state-of-the-art, namely logical structure recovery of articles and keyphrase extraction. Methodology. The first approach targets the issue of flat-format publishing. It performs a structural analysis of the camera-ready PDF article and recognises its fine-grained organisation over logical units. The second approach is the application of a keyphrase extraction algorithm that relies on rhetorical information from the recovered structure to better contour an article’s true points of focus. A recount of the scientific article’s function, content and structure is provided, along with insights into how different logical components such as section headings or the bibliography can be automatically identified and utilised for higher-quality keyphrase extraction. Findings. Structure recovery can be carried out independently of an article’s formatting specifics, by exploiting conventional dependencies between logical components. In addition, access to an article’s logical structure is beneficial across term extraction approaches, reducing input noise and facilitating the emphasis of regions of interest. Value. The first part of this work details a novel method for recovering the rhetorical structure of scientific articles that is competitive with state-of-the-art machine learning techniques, yet requires no layout-specific tuning or prior training. The second part showcases a keyphrase extraction algorithm that outperforms other solutions in an established benchmark, yet does not rely on collection statistics or external knowledge sources in order to be proficient.

For the last two decades Sweden has been one of the most important receiving countries for asylum seekers, hence regarding itself as a “humanitarian superpower”. Historically Sweden has had one of Europe’s most extensive migration policies and made its latest mark by 2015 by allowing the highest number of asylum seekers ever to the country. The media coverage and the public debate on the war refugee migration to Sweden has been comprehensive and thereby put the topic of migration in the centre of parliamentary discussions. The aim of this study was to examine if and how migration has been a subject of securitization in the parliamentary debates. The study was conducted by using a qualitative text analysis of parliament protocols from 2013–2015. The results show that during the period of study a number of parties have made securitising statements regarding migration, but that the subject of migration was securitized first in 2015 when the securitising problem formulation was adopted by a majority in the parliament. I have also concluded that there has been a slight change in the way in which migration is securitized. In 2013–2014 the majority of the parliament parties used the diffuse securitising technique when debating migration, while in 2015 there was a shift towards the exceptionalist securitising technique.

Magíster en Ingeniería de Negocios con Tecnologías de Información / El emprendimiento a nivel mundial y particularmente en chile presenta una problemática constante, debido a las dificultades propias de emprender se suma la necesidad de disponer de capitales de riesgo que posibiliten llevar adelante una idea de negocio. Si en lo anterior se considera que en nuestro país cerca del 96% de las empresas formalmente existentes corresponde a Mypes micros y pequeñas empresas las cuales por su tamaño no disponen de acceso directo a fuentes de financiación tradicionales, la propuesta de participar y ser acreedor de uno de los fondos concursables de capitales semillas, CORFO, Indap, capitales abeja o Crece, resulta ser una solución viable para mantener el emprendimiento o simplemente dejarlo olvidado en un baúl por falta de recursos económicos. CSASESORES es una organización creada en el año 2011 bajo el reconocimiento de esta necesidad, con el objetivo de ser un factor de cambio que permita aportar en el crecimiento de los emprendimientos y Mypes en Chile. En sus breves años la organización ha sido acreedora de uno de los capitales semillas de emprendimiento en la región metropolitana y ha aportado activamente en el desarrollo de más de 50 ideas de negocios que finalmente han resultado ganadoras en la asignación de capital de riesgo de capitales semilla SERCOTEC. Para apoyar esta iniciativa se ha diseñado un proyecto que pretende fundar las bases para los procesos de gestión de ideas de negocios de la organización creciente; permitiendo además implementar soluciones tecnológicas que posibiliten automatizar unos de los procesos más extensos, que corresponde a la evaluación, comprensión y mejora de emprendimientos que finalmente son presentados a postulaciones de fondos concursables de capitales semillas. Los resultados preliminares obtenidos son alentadores, ya que la aplicación de la técnica de Text Mining y Latent Semantic Analysis permitió identificar cerca de diez clúster con sus temáticas durante el proceso de evaluación en los ámbitos de las fortalezas y debilidades de iniciativas de capitales semillas. Junto con lo anterior se logró descubrir un conjunto de relaciones semánticas estrechas, tanto en las fortalezas como también en el ámbito de las debilidades de las iniciativas evaluadas, estas relaciones se encuentran visibles y documentadas gracias a la utilización de la técnica de Latent Semantic Analysis. / 14/7/2021