Extraction of semantic relations from English text is the topic of this thesis. It focuses on exploitation of a dependency parser. A method based on syntactic patterns is proposed and evaluated in addition to evaluation of several statistical methods over syntactic features. It applies the methods for extraction of a hypernymy relation and evaluates it on the WordNet thesaurus. A system for extraction of semantic relations from text is designed and implemented based on these methods.

Risk assessment is essential for nuclear power plants (NPPs) due to the complex dynamic nature of such systems-of-systems, as well as the devastating impacts of nuclear accidents on the environment, public health, and economy. Lessons learned from the Fukushima nuclear accident demonstrated the importance of enhancing current risk assessment methodologies and developing efficient early warning decision support tools. Static probabilistic risk assessment (PRA) techniques (e.g., event and fault tree analysis) have been extensively adopted in nuclear applications to ensure NPPs comply with safety regulations. However, numerous studies have highlighted the limitations of static PRA methods such as the lack of considering the dynamic hardware/software/operator interactions inside the NPP and the timing/sequence of events. In response, several dynamic probabilistic risk assessment (DPRA) methodologies have been developed and continuously evolved over the past four decades to overcome the limitations of static PRA methods. DPRA presents a comprehensive approach to assess the risks associated with complex, dynamic systems. However, current DPRA approaches are faced with challenges associated with the intra/interdependence within/between different NPP complex systems and the massive amount of data that needs to be analyzed and rapidly acted upon. In response to these limitations of previous work, the main objective of this dissertation is to develop a physics-based DPRA platform and an intelligent data-driven prediction tool for NPP safety enhancement under normal and abnormal operating conditions. The results of this dissertation demonstrate that the developed DPRA platform is capable of simulating the dynamic interaction between different NPP systems and estimating the temporal probability of core damage under different transients with significant analysis advantages from both the computational time and data storage perspectives. The developed platform can also explicitly account for uncertainties associated with the NPP's physical parameters and operating conditions on the plant's response and probability of its core damage. Furthermore, an intelligent decision support tool, developed based on artificial neural networks (ANN), can significantly improve the safety of NPPs by providing the plant operators with fast and accurate predictions that are specific to such NPP. Such rapid prediction will minimize the need to resort to idealized physics-based simulators to predict the underlying complex physical interactions. Moving forward, the developed ANN model can be trained under plant operational data, plants operating experience database, and data from rare event simulations to consider for example plant ageing with time, operational transients, and rare events in predicting the plant behavior. Such intelligent tool can be key for NPP operators and managers to take rapid and reliable actions under abnormal conditions. / Thesis / Doctor of Philosophy (PhD)

No description available.

In the field of public diplomacy, it has increasingly become relevant to develop analytical operations in order to gain knowledge on the perceptions of foreign publics and their attitudes towards countries – a construct known as the country image. In recent time, research on public diplomacy has been increasingly occupied with the impact of media on country images due to the continuous expansion and fragmentation of the hybrid media landscape. Academics and practitioners alike must navigate through large quantities of data and different choices regarding prioritization of sources and methods in order to find suitable analytical frameworks to properly investigate the country image as an analytical object. This thesis addresses these analytical challenges by developing a diachronic text mining analysis of the Swedish country image in the British newspaper the Guardian. Sweden has in recent years drawn attention from the international media, for example during the so-called refugee crisis of 2015 and during the covid-19 pandemic of 2020. In the extensive media coverage of these major events the Swedish course of action was met with a wide range approval and criticism. Using a mixed-method approach of distant and close reading, this thesis approaches the news coverage of Sweden in the Guardian through a content analysis designed in three analytical steps: topic modelling, collocation and concordance analysis, and diachronic corpus assisted discourse analysis. In each of these steps, the appearance of different dimensions of the country image was explored using a dimensional model for integrative country image analysis developed by Ingenhoff and Buhmann. The design of the mixed-method approach showcase how large quantities of textual data can be analyzed in a new diachronic approach, bringing strains of research from digital humanities to the field of public diplomacy.

During the past several decades, journalists have been struggling with the ever growing amount of data on the internet. Investigating the validity of the sources or finding similar articles for a story can consume a lot of time and effort. These issues are even amplified by the declining size of the staff of news agencies. The solution is to empower the remaining professional journalists with digital tools created by computer scientists. This thesis project is inspired by an idea to provide software support for journalistic work with interactive visual interfaces and artificial intelligence. More specifically, within the scope of this thesis project, we created a backend module that supports several text mining methods such as keyword extraction, named entity recognition, sentiment analysis, fake news classification and also data collection from various data sources to help professionals in the field of journalism. To implement our system, first we gathered the requirements from several researchers and practitioners in journalism, media studies, and computer science, then acquired knowledge by reviewing literature on current approaches. Results are evaluated both with quantitative methods such as individual component benchmarks and also with qualitative methods by analyzing the outcomes of the semi-structured interviews with collaborating and external domain experts. Our results show that there is similarity between the domain experts' perceived value and the performance of the components on the individual evaluations. This shows us that there is potential in this research area and future work would be welcomed by the journalistic community.

Manually grading essays and providing comprehensive feedback pose significant challenges for writing instructors, requiring subjective assessments of various writing elements. Automated essay scoring (AES) systems have emerged as a potential solution, offering improved grading consistency and time efficiency, along with insightful analytics. However, the use of AES in English as a Second Language (ESL) remains rare. This dissertation aims to explore the implementation of AES in ESL education to enhance teaching and learning. The dissertation presents a study involving ESL teachers who learned to use a specific AES system called LightSide, a free and open text mining tool, to enhance writing instruction. The study involved observations, interviews, and a workshop where teachers learned to build their own AES using LightSide. The study aimed to address questions related to teacher interest in using AES, challenges faced by teachers, and the influence of the workshop on teachers' perceptions of AES. By exploring the use of AES in ESL education, this research provides valuable insights to inform the integration of technology and enhance the teaching and learning of writing skills for English language learners.

La crise de la COVID-19 a provoqué des bouleversements majeurs dans la vie des populations du monde entier et a suscité des réactions sociales importantes. La propagation du virus contagieux de la COVID-19 a été rapidement suivie d’une « épidémie » d’explications et de discours tentant de donner un sens à la crise. Lorsqu’un événement dévastateur se produit, les gens se demandent ce qui se passe et ce que cela signifie. Le premier but de cette recherche est de suivre l’évolution de la dynamique du blâme et de la désignation de boucs émissaires au fur et à mesure que la pandémie de COVID-19 se déroule. Le deuxième but de cette recherche est d’évaluer l’intérêt d’utiliser l’Allocation de Dirichlet latente (ADL), un modèle de mélange/classe latente génératif bayésien, dans l’analyse de questions ouvertes. Les données ont été recueillies auprès d’un échantillon représentatif de 3617 Canadiens selon un devis de recherche longitudinal intensif (avec 12 temps de mesure). Neuf thématiques ont été identifiées, dont six sont récurrentes à différents temps de mesure. Les résultats indiquent que, durant les premiers mois de la pandémie, les Canadiens blâment majoritairement les collectivités distantes, telles que la Chine et les marchés aux animaux vivants (wet markets). Au fil du temps, ils blâment de plus en plus les collectivités locales, tels que les individus qui ne respectent pas les mesures sanitaires. Cette recherche met en évidence le rôle de la proximité géographique et de l’évaluation du risque dans la manière dont le public perçoit la pandémie. / The COVID-19 crisis has caused major disruptions in the lives of populations around the globe and provoked important social responses. The spread of the contagious COVID-19 virus was quickly followed by an outbreak of explanations and discourses trying to make sense of the crisis. When devastating events occur, people ask themselves what happened, why the event happened and what it means. The first goal of this paper is to track the changing dynamics of blame attribution and scapegoating as the COVID-19 pandemic unfolds. The second goal of this paper is to evaluate the relevance of LDA (Latent Dirichlet Allocation), a Bayesian generative mixture/latent class model, to analyze open-ended survey responses. Data was collected from a representative sample of 3,617 Canadians following an intensive longitudinal research design (with 12 waves). Nine topics were identified, six of which were recurring. Canadians mostly blame distant collectives in the early months of the pandemic, especially China and wet markets. Over time, they increasingly blame local collectives, such as individuals who do not comply with sanitary measures. This study highlights the role of geographic proximity and perceived risk in shaping public perceptions of the pandemic.

No description available.

No description available.

Nature's non-material contributions to people are difficult to quantify and one aspect in particular, nature's contributions to communication (NCC), has so far been neglected. Recent advances in automated language processing tools enable us to quantify diversity patterns underlying the distribution of plant and animal taxon labels in creative literature, which we term BiL (biodiversity in literature). We assume BiL to provide a proxy for people's openness to nature's non-material contributions enhancing our understanding of NCC. We assembled a comprehensive list of 240,000 English biological taxon labels. We pre-processed and searched a subcorpus of digitised literature on Project Gutenberg for these labels. We quantified changes in biodiversity indices commonly used in ecological studies for 16,000 books, encompassing 4,000 authors, as proxies for BiL between 1705 and 1969. We observed hump-shape patterns for taxon label richness, abundance and Shannon diversity indicating a peak of BiL in the middle of the 19th century. This is also true for the ratio of biological to general lexical richness. The variation in label use between different sections within books, quantified as β-diversity, declined until the 1830s and recovered little, indicating a less specialised use of taxon labels over time. This pattern corroborates our hypothesis that before the onset of industrialisation BiL may have increased, reflecting several concomitant influences such as the general broadening of literary content, improved education and possibly an intensified awareness of the starting loss of biodiversity during the period of romanticism. Given that these positive trends continued and that we do not find support for alternative processes reducing BiL, such as language streamlining, we suggest that this pronounced trend reversal and subsequent decline of BiL over more than 100 years may be the consequence of humans’ increasing alienation from nature owing to major societal changes in the wake of industrialisation. We conclude that our computational approach of analysing literary communication using biodiversity indices has a high potential for understanding aspects of non-material contributions of biodiversity to people. Our approach can be applied to other corpora and would benefit from additional metadata on taxa, works and authors.