The purpose of this thesis is to examine how instruction librarians‟ perceptions of web scale discovery services and their perceptions of the impact of web scale discovery on students‟ information literacy are reflected in their instruction practices in academic libraries. This study addresses how instruction librarians generally perceive WSDS and students‟ information literacy as well as how instruction librarians conduct their instruction sessions. The study‟s empirical data consists of the responses received through seven semi-structured interviews with ten instruction librarians at three separate universities in the Västra Götaland region. The study concludes that library instruction practices reflect many of the librarians‟ perceptions surrounding WSDS, most notably in the areas of source criticism and critical assessment of search results. The study also shows that the search strategies, techniques, and tools taught in instruction sessions are greatly influenced by librarians‟ perceptions of the usefulness of WSDS in student search processes. The librarians‟ responses demonstrate the importance of teaching students search strategies and techniques as well as helping them develop critical evaluation skills that will remain relevant as search technologies continue to evolve.

The purpose of this thesis is to examine how Swedish highereducation librarians mediate discovery tools to its users. Thisstudy aims to investigate higher education librarians’ generalattitudes towards discovery tools, their perception ofstudents’ discovery tool usage, and how they mediatediscovery tools to students through reference interviews anduser training. The empirical ground of this study is a semistructuredsurvey that was answered by 115 instructinglibrarians. The study concludes that although librariansgenerally have a critical attitude towards discovery tools theyalso think that the discovery tools serve a useful purpose,especially as a starting point in the information searchprocess. The study also concludes that librarians generallyperceive students’ attitudes towards discovery tools aspositive, although students do not always use the tools totheir full potential. The librarians also raised the importanceof user training that is less focused on teaching searchtechniques and more focused on information literacy. / Program: Bibliotekarie

Natural Language Processing (NLP) develops computational approaches to processing language data. Supervised machine learning has become the dominant methodology of modern NLP. The performance of a supervised NLP system crucially depends on the amount of data available for training. In the standard supervised framework, if a sequence of words was not encountered in the training set, the system can only guess at its label at test time. The cost of producing labeled training examples is a bottleneck for current NLP technology. On the other hand, a vast quantity of unlabeled data is freely available. This dissertation proposes effective, efficient, versatile methodologies for 1) extracting useful information from very large (potentially web-scale) volumes of unlabeled data and 2) combining such information with standard supervised machine learning for NLP. We demonstrate novel ways to exploit unlabeled data, we scale these approaches to make use of all the text on the web, and we show improvements on a variety of challenging NLP tasks. This combination of learning from both labeled and unlabeled data is often referred to as semi-supervised learning. Although lacking manually-provided labels, the statistics of unlabeled patterns can often distinguish the correct label for an ambiguous test instance. In the first part of this dissertation, we propose to use the counts of unlabeled patterns as features in supervised classifiers, with these classifiers trained on varying amounts of labeled data. We propose a general approach for integrating information from multiple, overlapping sequences of context for lexical disambiguation problems. We also show how standard machine learning algorithms can be modified to incorporate a particular kind of prior knowledge: knowledge of effective weightings for count-based features. We also evaluate performance within and across domains for two generation and two analysis tasks, assessing the impact of combining web-scale counts with conventional features. In the second part of this dissertation, rather than using the aggregate statistics as features, we propose to use them to generate labeled training examples. By automatically labeling a large number of examples, we can train powerful discriminative models, leveraging fine-grained features of input words.

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Nous abordons dans cette thèse quelques-unes des questions soulevées par I'émergence d'applications sociales sur le Web, en se concentrant sur deux axes importants: l'efficacité de recherche sociale dans les applications Web et l'inférence de liens sociaux signés à partir des interactions entre les utilisateurs dans les applications Web collaboratives. Nous commençons par examiner la recherche sociale dans les applications de "tag- ging". Ce problème nécessite une adaptation importante des techniques existantes, qui n'utilisent pas des informations sociaux. Dans un contexte ou le réseau est importante, on peut (et on devrait) d'exploiter les liens sociaux, ce qui peut indiquer la façon dont les utilisateurs se rapportent au demandeur et combien de poids leurs actions de "tagging" devrait avoir dans le résultat. Nous proposons un algorithme qui a le potentiel d'évoluer avec la taille des applications actuelles, et on le valide par des expériences approfondies. Comme les applications de recherche sociale peut être considérée comme faisant partie d'une catégorie plus large des applications sensibles au contexte, nous étudions le problème de répondre aux requêtes à partir des vues, en se concentrant sur deux sous-problèmes importants. En premier, la manipulation des éventuelles différences de contexte entre les différents points de vue et une requête d'entrée conduit à des résultats avec des score incertains, valables pour le nouveau contexte. En conséquence, les algorithmes top-k actuels ne sont plus directement applicables et doivent être adaptés aux telle incertitudes dans les scores des objets. Deuxièmement, les techniques adaptées de sélection de vue sont nécessaires, qui peuvent s’appuyer sur les descriptions des requêtes et des statistiques sur leurs résultats. Enfin, nous présentons une approche pour déduire un réseau signé (un "réseau de confiance") à partir de contenu généré dans Wikipedia. Nous étudions les mécanismes pour deduire des relations entre les contributeurs Wikipédia - sous forme de liens dirigés signés - en fonction de leurs interactions. Notre étude met en lumière un réseau qui est capturée par l’interaction sociale. Nous examinons si ce réseau entre contributeurs Wikipedia représente en effet une configuration plausible des liens signes, par l’étude de ses propriétés globaux et locaux du reseau, et en évaluant son impact sur le classement des articles de Wikipedia. / We address in this thesis some of the issues raised by the emergence of social applications on the Web, focusing on two important directions: efficient social search inonline applications and the inference of signed social links from interactions between users in collaborative Web applications. We start by considering social search in tagging (or bookmarking) applications. This problem requires a significant departure from existing, socially agnostic techniques. In a network-aware context, one can (and should) exploit the social links, which can indicate how users relate to the seeker and how much weight their tagging actions should have in the result build-up. We propose an algorithm that has the potential to scale to current applications, and validate it via extensive experiments. As social search applications can be thought of as part of a wider class of context-aware applications, we consider context-aware query optimization based on views, focusing on two important sub-problems. First, handling the possible differences in context between the various views and an input query leads to view results having uncertain scores, i.e., score ranges valid for the new context. As a consequence, current top-k algorithms are no longer directly applicable and need to be adapted to handle such uncertainty in object scores. Second, adapted view selection techniques are needed, which can leverage both the descriptions of queries and statistics over their results. Finally, we present an approach for inferring a signed network (a "web of trust")from user-generated content in Wikipedia. We investigate mechanisms by which relationships between Wikipedia contributors - in the form of signed directed links - can be inferred based their interactions. Our study sheds light into principles underlying a signed network that is captured by social interaction. We investigate whether this network over Wikipedia contributors represents indeed a plausible configuration of link signs, by studying its global and local network properties, and at an application level, by assessing its impact in the classification of Wikipedia articles.javascript:nouvelleZone('abstract');_ajtAbstract('abstract');

This diploma thesis discusses a modern concept of information retrieval and search engines for libraries and other academic institutions. The concept named "web scale discovery" comprises of search engines, whose main characteristics lie in simplicity and user friendliness for the end users while maintaining all functional qualities of traditional research databases. Users can search in a wide variety of international research databases, and also in local sources of an institution that are combined within a large central index. The theoretical section presents definitions of web scale discovery, which conceptually set the new method of information retrieval within the field of information and library science. A graphic scheme of the basic functionality of a web scale discovery system is presented. Also discussed are requirements for a modern discovery system, an overview of the current situation in the Czech Republic, and a short characteristic of commercially available discovery systems. The theoretical part concludes with a literature review of selected foreign research, studying user satisfaction with the new solution, the impact on electronic and print resources in libraries, and usability testing. The practical part presents an original research study - usability testing of EBSCO Discovery...