Medienkompetenz - Kompetenz für neue Medien:Studie im Auftrag des Forum Bildung ; Workshop am 14. September 2001 in Berlin

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Nachhaltigkeit von E-Learning-Innovationen: Von der Pionierphase zur nachhaltigen Implementierung

In den letzten Jahren wurden an den meisten Hochschulen im In- und Ausland E-Learning-Projekte in die Wege geleitet. Nach den Projekt- und Impulsprogrammen folgen die Konsolidierungsphasen. Schon während der Projektphasen zeigen sich Problemgruppen, die darauf verweisen, dass E-Learning nicht «nur» eine Angelegenheit der Pädagogik und Didaktik ist. Der erste Teil des Aufsatzes benennt einzelne dieser Problemfelder. Der zweite Teil zeigt auf, dass E-Learning die Hochschulen als Ganzes betrifft, und unterbreitet ein Rahmenkonzept, in dem gezeigt wird, wie die benannten Probleme bei der Implementierung von E-Learning vermieden werden können

Towards a Universal Wordnet by Learning from Combined Evidenc

Lexical databases are invaluable sources of knowledge about words and their meanings, with numerous applications in areas like NLP, IR, and AI. We propose a methodology for the automatic construction of a large-scale multilingual lexical database where words of many languages are hierarchically organized in terms of their meanings and their semantic relations to other words. This resource is bootstrapped from WordNet, a well-known English-language resource. Our approach extends WordNet with around 1.5 million meaning links for 800,000 words in over 200 languages, drawing on evidence extracted from a variety of resources including existing (monolingual) wordnets, (mostly bilingual) translation dictionaries, and parallel corpora. Graph-based scoring functions and statistical learning techniques are used to iteratively integrate this information and build an output graph. Experiments show that this wordnet has a high level of precision and coverage, and that it can be useful in applied tasks such as cross-lingual text classification

Leveraging Semantic Annotations to Link Wikipedia and News Archives

The incomprehensible amount of information available online has made it difficult to retrospect on past events. We propose a novel linking problem to connect excerpts from Wikipedia summarizing events to online news articles elaborating on them. To address the linking problem, we cast it into an information retrieval task by treating a given excerpt as a user query with the goal to retrieve a ranked list of relevant news articles. We find that Wikipedia excerpts often come with additional semantics, in their textual descriptions, representing the time, geolocations, and named entities involved in the event. Our retrieval model leverages text and semantic annotations as different dimensions of an event by estimating independent query models to rank documents. In our experiments on two datasets, we compare methods that consider different combinations of dimensions and find that the approach that leverages all dimensions suits our problem best

fh-presse April 2018

Ausgabe 2/2018 der fh-press

Der Turing-Spielplatz

Reden wir von Turing, aber nicht von der Galaxis. 1993 kommentiert Wolfgang Coy Turings Idee einer Maschine, die wie Menschen denkt. Eine Person, die über ein Terminal mit einer Testinstanz verbunden ist, könnte nach Turings Gedankenexperiment in diesem Fall nicht unterscheiden, ob am anderen Ende der Leitung ein Mensch oder ein Apparat auf ihre Eingabe reagiert. Die Bemerkung Coys lokalisiert diesen Gedanken in seinem sozio-ökonomischen Umfeld: „Turings Imitationsspiel ist ein Gesellschaftsspiel; er beschreibt die erstarrte bürgerliche Nachkriegsgesellschaft wie er sie sieht: als Automaten.

Diversifying Search Results Using Time

Getting an overview of a historic entity or event can be difficult in search results, especially if important dates concerning the entity or event are not known beforehand. For such information needs, users would benefit if returned results covered diverse dates, thus giving an overview of what has happened throughout history. Diversifying search results based on important dates can be a building block for applications, for instance, in digital humanities. Historians would thus be able to quickly explore longitudinal document collections by querying for entities or events without knowing associated important dates apriori. In this work, we describe an approach to diversify search results using temporal expressions (e.g., in the 1990s) from their contents. Our approach first identifies time intervals of interest to the given keyword query based on pseudo-relevant documents. It then re-ranks query results so as to maximize the coverage of identified time intervals. We present a novel and objective evaluation for our proposed approach. We test the effectiveness of our methods on the New York Times Annotated corpus and the Living Knowledge corpus, collectively consisting of around 6 million documents. Using history-oriented queries and encyclopedic resources we show that our method indeed is able to present search results diversified along time