QA-EL

QA-EL

Mining Lexical-Semantic Knowledge from Dynamic and Linguistic Sources and Integration into Question Answering for Discourse-Based Knowledge Acquisition in eLearning

Information overload is a well-known problem which also affects learning, since huge amounts of learning material are nowadays available in different formats and from different sources.

This makes it all the harder for the learner to access information in a fast and direct way.

Goal

In the QA-EL project we investigate new applications of dynamic lexical-semantic resources for information search in eLearning. On the one hand, we develop novel ways of mining knowledge from Web 2.0 knowledge repositories. On the other hand, we apply Question Answering in the area of discourse-based knowledge acquisition in eLearning for the first time.

Our goal is to provide uniform access to both institutional and informal knowledge resources, whereby precise and short aggregated answers are supplied to the learner.

Feel free to download our QA-EL flyer.

System Architecture

Our system architecture focuses on the integration of information extracted from different knowledge repositories for the targeted needs of Question Answering in eLearning 2.0.

Classical linguistically motivated resources such as GermaNet are coupled with lexical-semantic information extracted from collaborative resources like Wikipedia, and put into service for processing heterogeneous institutional and other Web 2.0 eLearning content.

People

  • Prof. Iryna Gurevych, Principal Investigator
  • Silvana Hartmann, Doctoral Researcher
  • Elisabeth Wolf, Project Coordinator

Funding

The project is funded by the German Research Foundation (DFG). It was originally initiated as part of the Young Researcher's Excellence Emmy-Noether Program.