The aim of this thesis is to find out how co-referential relationships in Czech texts are developed in terms of the information and constituent structures. The data was processed using the following methods. Firstly I examined the available literature on information and constituent structures with special attention being paid to the formation of coreferential relations. This information was then compared to my own research. With regards to disparities between the approaches, it was necessary to introduce some basic terms that are used within my thesis (Chapter 2). Thereafter, I was able to introduce current approaches to the thesis, including the methodological basis of my work, which is represented by the foreign Centering Theory, because unlike other Czech approaches this theory includes predictions on the frequency of different ways of co-referential relationship development (Chapter 3). Secondly, the knowledge gained from literature was applied to two different types of data: corpus material of Prague Dependency Treebank and material obtained through a questionnaire survey (Chapter 4). The most appropriate methods of developing co-referential chains in the Czech text were determined by challenging the prediction model with the obtained data set. Using this method we gained information about...

Purpose: The purpose of the study is to examine whether the prioritization of subject-specific disclosures in Swedish companies' sustainability reports, which report in accordance with GRI Standards, is in line with previous research where GRI standards have been investigated. Theory: The theoretical frame of reference for the study is formed around legitimacy theory, stakeholder theory and institutional theory. Sustainability reporting is a part of the legitimacy-creating activities that are relevant to the explicit as well as implicit demands on companies in society, and companies in similar industries may tend to imitate or resembleeach other by isomorphism. Method: A quantitative research method is used and together with a content analysis meansto study how sector affiliation affects the prioritization of subject-specific disclosures. The priority measurements used in the study are the topic areas Economy, Environment and Social, which are measured through companies’ materiality analysis and GRI-index. The final sample for the study consists of all companies that reported sustainability in accordance with GRI Standards in 2019 in Sweden after sample-loss, which resulted in a total of 72 companiesand 20 sectors. Collection of empirical data has been produced using the GRI Sustainability Disclosure Database. Conclusion: The empirical results indicate a connection between sector affiliation and companies' choice of subject-specific disclosures in Sweden, where at least one of three disclosures must be prioritized for the sustainability report to be prepared in accordance with GRI Standards. The GRI Standards GRI-index and materiality analysis clarify which subjectarea is prioritized. The study shows that the sector division Financial services prioritizes economic sustainability, Transport, communication and services prioritizes social sustainability and the remaining sector divisions prioritize environmental sustainability. The results are largely in line with previous research that examined GRI standards, where similar results regarding this connection have been demonstrated. / Syfte: Studiens syfte är att undersöka huruvida prioriteringen av ämnesspecifika upplysningar i svenska företags hållbarhetsrapporter, som rapporterar i enlighet med GRI Standards, är i fas med tidigare forskning där GRI-standarder undersökts. Teori: Den teoretiska referensramen för studien formas kring legitimitetsteorin, intressentteorin och institutionell teori. Hållbarhetsrapportering är en del av de legitimitetsskapande aktiviteter som är relevanta för de explicita såväl som implicita kraven som finns på företag i samhället, samt att företag inom likartade sektorer kan tendera att härma eller likna varandra genom olika typer av isomorfism.  Metod: En kvantitativ forskningsmetod används för att med en innehållsanalys undersöka hur sektortillhörighet påverkar prioritering av ämnesspecifika upplysningar i företags hållbarhetsrapporter. För att mäta prioritering i denna studie används ämnesområdena Ekonomi, Miljö, och Social, vilka kvantifieras genom företagens väsentlighetsanalys samt GRI-index. Det slutliga urvalet för studien utgörs av alla företag som hållbarhetsrapporterade i enlighet med GRI Standards år 2019 i Sverige efter bortfall, vilket resulterade i totalt 72 företag och 20 sektorer. Insamling av empiri är framtaget med hjälp av databasen GRI Sustainability Disclosure Database.  Slutsats: Det empiriska resultatet tyder på ett samband mellan sektortillhörighet och företagens val av ämnesspecifika upplysningar i Sverige, där minst en av tre upplysningar måste prioriteras för att hållbarhetsrapporten ska vara upprättad i enlighet med GRI Standards. Genom GRI Standards GRI-index och väsentlighetsanalys tydliggörs vilket ämnesområde som prioriteras. Studien visar att sektorindelningen Finansiella tjänster prioriterar ekonomisk hållbarhet, Transport, kommunikation och tjänster prioriterar social hållbarhet och resterande sektorindelningar prioriterar miljömässig hållbarhet. Resultatet är till större del i fas med tidigare forskning som undersökt GRI-standarder, där liknande resultat gällande detta samband påvisats.

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In recent years, the most popular web search engines have excelled in their ability to answer short queries that require clear, localized and personalized answers. When it comes to complex exploratory search tasks however, the main challenge for the searcher remains the same as back in the 1990s: Trying to formulate a single query that contains all the right keywords to produce at least some relevant results. In this work we want to investigate new ways to facilitate exploratory search by making use of context information from the user's entire search process. Therefore we present the concept of session-based semantic search, with an optional extension to collaborative search scenarios. To improve the relevance of search results we expand queries with terms from the user's recent query history in the same search context (session-based search). We introduce a novel method for query classification based on statistical topic models which allows us to track the most important topics in a search session so that we can suggest relevant documents that could not be found through keyword matching. To demonstrate the potential of these concepts, we have built the prototype of a session-based semantic search engine which we release as free and open source software. In a qualitative user study that we have conducted, this prototype has shown promising results and was well-received by the participants.:1. Introduction 2. Related Work 2.1. Topic Models 2.1.1. Common Traits 2.1.2. Topic Modeling Techniques 2.1.3. Topic Labeling 2.1.4. Topic Graph Visualization 2.2. Session-based Search 2.3. Query Classification 2.4. Collaborative Search 2.4.1. Aspects of Collaborative Search Systems 2.4.2. Collaborative Information Retrieval Systems 3. Core Concepts 3.1. Session-based Search 3.1.1. Session Data 3.1.2. Query Aggregation 3.2. Topic Centroid 3.2.1. Topic Identification 3.2.2. Topic Shift 3.2.3. Relevance Feedback 3.2.4. Topic Graph Visualization 3.3. Search Strategy 3.3.1. Prerequisites 3.3.2. Search Algorithms 3.3.3. Query Pipeline 3.4. Collaborative Search 3.4.1. Shared Topic Centroid 3.4.2. Group Management 3.4.3. Collaboration 3.5. Discussion 4. Prototype 4.1. Document Collection 4.1.1. Selection Criteria 4.1.2. Data Preparation 4.1.3. Search Index 4.2. Search Engine 4.2.1. Search Algorithms 4.2.2. Query Pipeline 4.2.3. Session Persistence 4.3. User Interface 4.4. Performance Review 4.5. Discussion 5. User Study 5.1. Methods 5.1.1. Procedure 5.1.2. Implementation 5.1.3. Tasks 5.1.4. Questionnaires 5.2. Results 5.2.1. Participants 5.2.2. Task Review 5.2.3. Literature Research Results 5.3. Discussion 6. Conclusion Bibliography Weblinks A. Appendix A.1. Prototype: Source Code A.2. Survey A.2.1. Tasks A.2.2. Document Filter for Google Scholar A.2.3. Questionnaires A.2.4. Participant’s Answers A.2.5. Participant’s Search Results / Die führenden Web-Suchmaschinen haben sich in den letzten Jahren gegenseitig darin übertroffen, möglichst leicht verständliche, lokalisierte und personalisierte Antworten auf kurze Suchanfragen anzubieten. Bei komplexen explorativen Rechercheaufgaben hingegen ist die größte Herausforderung für den Nutzer immer noch die gleiche wie in den 1990er Jahren: Eine einzige Suchanfrage so zu formulieren, dass alle notwendigen Schlüsselwörter enthalten sind, um zumindest ein paar relevante Ergebnisse zu erhalten. In der vorliegenden Arbeit sollen neue Methoden entwickelt werden, um die explorative Suche zu erleichtern, indem Kontextinformationen aus dem gesamten Suchprozess des Nutzers einbezogen werden. Daher stellen wir das Konzept der sitzungsbasierten semantischen Suche vor, mit einer optionalen Erweiterung auf kollaborative Suchszenarien. Um die Relevanz von Suchergebnissen zu steigern, werden Suchanfragen mit Begriffen aus den letzten Anfragen des Nutzers angereichert, die im selben Suchkontext gestellt wurden (sitzungsbasierte Suche). Außerdem wird ein neuartiger Ansatz zur Klassifizierung von Suchanfragen eingeführt, der auf statistischen Themenmodellen basiert und es uns ermöglicht, die wichtigsten Themen in einer Suchsitzung zu erkennen, um damit weitere relevante Dokumente vorzuschlagen, die nicht durch Keyword-Matching gefunden werden konnten. Um das Potential dieser Konzepte zu demonstrieren, wurde im Rahmen dieser Arbeit der Prototyp einer sitzungsbasierten semantischen Suchmaschine entwickelt, den wir als freie Software veröffentlichen. In einer qualitativen Nutzerstudie hat dieser Prototyp vielversprechende Ergebnisse hervorgebracht und wurde von den Teilnehmern positiv aufgenommen.:1. Introduction 2. Related Work 2.1. Topic Models 2.1.1. Common Traits 2.1.2. Topic Modeling Techniques 2.1.3. Topic Labeling 2.1.4. Topic Graph Visualization 2.2. Session-based Search 2.3. Query Classification 2.4. Collaborative Search 2.4.1. Aspects of Collaborative Search Systems 2.4.2. Collaborative Information Retrieval Systems 3. Core Concepts 3.1. Session-based Search 3.1.1. Session Data 3.1.2. Query Aggregation 3.2. Topic Centroid 3.2.1. Topic Identification 3.2.2. Topic Shift 3.2.3. Relevance Feedback 3.2.4. Topic Graph Visualization 3.3. Search Strategy 3.3.1. Prerequisites 3.3.2. Search Algorithms 3.3.3. Query Pipeline 3.4. Collaborative Search 3.4.1. Shared Topic Centroid 3.4.2. Group Management 3.4.3. Collaboration 3.5. Discussion 4. Prototype 4.1. Document Collection 4.1.1. Selection Criteria 4.1.2. Data Preparation 4.1.3. Search Index 4.2. Search Engine 4.2.1. Search Algorithms 4.2.2. Query Pipeline 4.2.3. Session Persistence 4.3. User Interface 4.4. Performance Review 4.5. Discussion 5. User Study 5.1. Methods 5.1.1. Procedure 5.1.2. Implementation 5.1.3. Tasks 5.1.4. Questionnaires 5.2. Results 5.2.1. Participants 5.2.2. Task Review 5.2.3. Literature Research Results 5.3. Discussion 6. Conclusion Bibliography Weblinks A. Appendix A.1. Prototype: Source Code A.2. Survey A.2.1. Tasks A.2.2. Document Filter for Google Scholar A.2.3. Questionnaires A.2.4. Participant’s Answers A.2.5. Participant’s Search Results

Résumé :Introduction: Les recherches sur l’Education Thérapeutique du Patient (ETP) montrent qu’elle peut encore gagner à développer l’ensemble des compétences des patients en utilisant des méthodes pédagogiques interactives. La simulation est rarement utilisée pour aider les personnes atteintes de maladies chroniques à développer ces compétences. L’objectif du travail de thèse était de promouvoir l'utilisation de la simulation dans l'éducation thérapeutique des patients (S-ETP) par : i) la réalisation d’une conférence de consensus sur le sujet et ii) la réalisation d’une étude pilote de la S-ETP.Méthode: Un consensus d'experts a été obtenu avec la participation des patients et des proches aidants experts; des professionnels de la santé spécialisés experts en (ETP) et des experts en simulation. Chaque recommandation a été soumise à un examen exhaustif de la littérature. La qualité des éléments de preuve et la solidité des recommandations ont été évaluées au moyen de critères d'évaluation [GRADE]. L’étude pilote est réalisée sur un échantillon de 24 patients diabètiques dans le but de tester sa faisabilité.Résultats: A la fin du consensus, les experts ont identifié 26 recommandations spécifiques à l'utilisation de la S-ETP. Ils ont proposé des exemples de compétences dans différentes maladies et ont insisté sur l'importance d'adapter les conditions d'utilisation (emplacement, équipement, temps des soins) aux circonstances de l'apprenant et des compétences du patient à développer. Les experts doivent faire preuve d'une grande prudence car cette technique présente des conditions éthiques. L’essai de faisabilité a permis d’étudier la faisabilité de la S-ETP auprés des patients et des soignants.Conclusion: Ces recommandations soulignent le fait que la simulation pourrait apporter une valeur ajoutée en ETP. Elles fournissent le premier cadre pour l'utilisation de la simulation en ETP. La recherche sur la faisabilité et l'acceptabilité de l’utilisation de cette méthode auprès des patients, s’est basé sur le consensus d’expert. Cette étape était nécessaire avant d’évaluer l’efficience et d’établir une taxonomie de la S-ETP. / Abstract :Abstract :Introduction: Research in TPE shows that it can still benefit from developing the full range of patient skills through the use of interactive teaching methods. Simulation is rarely used to help individuals with chronic diseases to develop these skills. The objective of the thesis work was to promote the use of simulation in patient therapeutic education by: (i) conducting a consensus conference on the subject and (ii) conducting a pilot study of S-ETP.(S-TPE).Methods: Expert consensus was achieved with the participation of expert patients and caregivers; health professionals specialized in TPE; and simulation experts. Each recommendation was subject to an extensive literature review. The quality of the evidence and the strength of the recommendations were assessed through the evaluation, development and evaluation criteria categories [GRADE criteria].The pilot study is being conducted on a sample of 24 diabetic patients in order to test its feasibility.Results: At the end of the consensus, the experts identified 26 recommendations specific to the use of S-TPE. They proposed examples of skills in different diseases and stressed the importance of adapting the conditions of use (location, equipment, time of the care) to the circumstances of the patient learner and skills to be developed. Experts should exercise great caution as this technique presents ethical requirements.Conclusion: These recommendations underline the fact that simulation could bring added value to TPE. They provide the first framework of the use of simulation in TPE. Research on the feasibility and acceptability of using this method with patients was based on expert consensus. This step was necessary before assessing efficiency and establishing a taxonomy of the S-TPE.

This master's thesis is about the success of sharing economy in the accommodation segment in Prague. The thesis is based on theories conceptualizing sharing economy as a result of social and value change, not only as technological one. Using online review data, the user experience of shared accommodation via Airbnb and traditional via Booking are compared. Analysis is conducted with focus on users' satisfied needs and fulfilled values. For processing the data, text mining techniques (topic modelling and sentiment analysis) were employed. The major result is that in Prague the models of sharing economy accommodation meets the growing need in society to fulfil post-material values in the market much better than the models of traditional accommodation (hotels, hostels, boarding houses). In their experiences, Airbnb users reflect social and emotional values more often, even though most sharing economy accommodations in Prague do not involve any physical sharing with the host. The thesis thus brings a unique perspective on the Airbnb phenomenon in the Czech context and contributes to the discussion of why the market share of the sharing economy in the accommodation segment in Prague has been growing, while traditional models stagnated.

Exploring massive amount of user generated data with topics posits a new way to find useful information. The topics are assumed to be “hidden” and must be “uncovered” by statistical methods such as topic modeling. However, the user generated data is typically short and messy e.g. informal chat conversations, heavy use of slang words and “noise” which could be URL’s or other forms of pseudo-text. This type of data is difficult to process for most natural language processing methods, including topic modeling. This thesis attempts to find the approach that objectively give the better topics from short and messy text in a comparative study. The compared approaches are latent Dirichlet allocation (LDA), Re-organized LDA (RO-LDA), Gaussian Mixture Model (GMM) with distributed representation of words, and a new approach based on previous work named Neural Probabilistic Topic Modeling (NPTM). It could only be concluded that NPTM have a tendency to achieve better topics on short and messy text than LDA and RO-LDA. GMM on the other hand could not produce any meaningful results at all. The results are less conclusive since NPTM suffers from long running times which prevented enough samples to be obtained for a statistical test. / Att utforska enorma mängder användargenererad data med ämnen postulerar ett nytt sätt att hitta användbar information. Ämnena antas vara “gömda” och måste “avtäckas” med statistiska metoder såsom ämnesmodellering. Dock är användargenererad data generellt sätt kort och stökig t.ex. informella chattkonversationer, mycket slangord och “brus” som kan vara URL:er eller andra former av pseudo-text. Denna typ av data är svår att bearbeta för de flesta algoritmer i naturligt språk, inklusive ämnesmodellering. Det här arbetet har försökt hitta den metod som objektivt ger dem bättre ämnena ur kort och stökig text i en jämförande studie. De metoder som jämfördes var latent Dirichlet allocation (LDA), Re-organized LDA (RO-LDA), Gaussian Mixture Model (GMM) with distributed representation of words samt en egen metod med namnet Neural Probabilistic Topic Modeling (NPTM) baserat på tidigare arbeten. Den slutsats som kan dras är att NPTM har en tendens att ge bättre ämnen på kort och stökig text jämfört med LDA och RO-LDA. GMM lyckades inte ge några meningsfulla resultat alls. Resultaten är mindre bevisande eftersom NPTM har problem med långa körtider vilket innebär att tillräckligt många stickprov inte kunde erhållas för ett statistiskt test.

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The Swedish national bank has had shifting policies when it comes to publicity and confidentiality concerning publishing of texts within the bank. For some time, texts written by commissioners within the bank were decided to be published anonymously. Later they revoked the confidentiality policy, publishing all documents publicly again. This led to emerged interests in possible shifting attitudes toward topics discussed by the commissioners when writing anonymously versus publicly. On a request, based on the interests, there are ongoing analyses being conducted with the help of language technology where topics are extracted from the anonymous and public documents respectively. The aim is to find topics related to individual commissioners with the purpose of, as accurately as possible, identifying which of the anonymous documents is written by who. To discover unique relations between the commissioners and the generated topics, this thesis proposes hybrid topic based sentiment analysis as an author identification method to be able to use sentiments of topics as identifying features of commissioners. The results showed promise in the proposed approach. Though, further research is substantial, conducting comparisons with other acknowledged author identification methods, to confirm some level of efficacy, especially on documents containing close similarities among topics.

In the last decade, development of Information and Communication Technology (ICT) thatsupports online learning has increased the demand for e-learning and Massive Open OnlineCourses (MOOCs). Despite their increased popularity, MOOCs are struggling with highdropout rates and only a small percentage of learners complete the courses they enrolled in. Thepurpose of this thesis is to gain knowledge about MOOC learner behaviour. The aim of thestudy is to identify the motivations of learners and how these differ between learners whocompleted a course and those who dropped out. Research on MOOC learners has mostly beencarried out using a quantitative approach. While quantitative methodologies are effective inhandling the large amount of data produced by MOOCs, qualitative methods can give deeperinsights into online learners’ motivations. Therefore, this thesis employs an explanatorysequential mixed methods research, in which sentiment analysis and topic modeling of learnerreviews from the platform Coursera are further explained by qualitative interviews with MOOClearners. In the study 28,000 reviews scraped from five courses within the fields of data sciencewere analyzed and ten interviews were held with learners who either completed, dropped outfrom or both completed and dropped out from a MOOC. In the quantitative analysis nine coursefactors were found that learners wrote about: content, delivery, assessment, learning experience,tools, video material, teaching style, instructor skills and course provider. In addition, eighteenthemes were yielded from the interviews: self-discipline, just for fun, certificates, personaldevelopment, knowledge, career, time, equipment, practical exercise, interaction, instructor,reality, structure, external material, cost, community, degree of difficulty and other. In thediscussion the empirical findings are reflected upon using the theoretical framework of theresearch and the literature review. The result does not reveal any differences in motivationsbetween learners who completed a course and those who dropped out, however, it does identifyfactors that caused learners’ to drop out and the topics that most negative learner reviews wereabout. This research contributes to the body of knowledge in the field of research on MOOClearner retention and motivations. The topic is relevant for research in education informaticsand for continued improvements in delivery of MOOCs.