BibSonomy ::  user :: griesbau ::

AQUA - Automatic Quality Assessment and Feedback in eLearning 2.0 The current development of Web 2.0 makes the distinction between author and reader fading away. Users now produce huge amounts of data which sometimes is of questionable quality. This leads to the problem of information overload: how to make the most of this information without overwhelming the users? One key challenge to solve this issue is to assess the quality of the user generated content. In AQUA, we seek to develop algorithms to assess the quality of content automatically. We focus on two sources for this assessment: (1) user generated content; (2) feedback by users of the content. To do so, we investigate techniques from the fields of natural language processing (NLP), information retrieval, and machine learning. So, in a nutshell, AQUA will answer the following questions: What is quality of information? How does it matter in information search? How to model the quality of user generated content? How far can you go with automatic methods in assessing quality? How to give feedback to users regarding quality? The AQUA project is associated with the project "Mining Lexical-Semantic Knowledge from Dynamic and Linguistic Sources and Integration into Question Answering for Discourse-Based Knowledge Acquisition in e-learning (QA-EL)".

Educational Natural Language Processing (e-NLP) aims at finding new applications of Natural Language Processing for educational purposes, and developing new techniques and software taking into account the specific needs in the educational domain. We especially focus on NLP applications for eLearning 2.0, which is characterized by a worldwide learning community where educational material is produced both by students and teachers. This brings about new challenges for NLP since the amount of user-generated discourse and social media content such as wikis and blogs is constantly growing and requires intelligent automatic processing.

Bibliotheks- und Informationswissenschaft klingt zunächst wie ein Studium von vorgestern, hat aber mit dem "alten" Medium Buch kaum etwas zu tun. Denn Bibliotheks- und Informationswissenschaftler sind in Wirklichkeit die Informationsspezialisten von übermorgen: Sie erstellen, verwalten und gestalten Wissens- und Informationsangebote im Netz oder ganz klassisch in Bibliotheken und Archiven, und zwar so, dass jeder schnell und gezielt das findet, was er wirklich sucht.

The Lemur Project develops search engines, browser toolbars, text analysis tools, and data resources that support research and development of information retrieval and text mining software. The project is best known for its Indri search engine, Lemur Toolbar, and ClueWeb09 dataset. Our software and datasets are used widely in scientific and research applications, as well as in some commercial applications. The Lemur Project's software development philosophy emphasizes state-of-the-art accuracy, flexibility, and efficiency. For example, the Indri search engine provides accurate search for large text collections 'out of the box', and data is stored in an accessible manner to support development of new retrieval strategies. Software from the Lemur Project is distributed under open-source licenses that provide flexibility to scientists and software developers.