The idea of the web is to form an open space for collaboration, where the distinction between author and reader fades away. With the current development of Web 2.0, this is actually happening at a broad scale, e.g.:
- People write blogs.
- Wikis are in widespread use.
- Collaborative tagging systems rival professionally built taxonomies.
The users of Web 2.0 now produce huge amounts of information of diverse 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, i.e. user generated meta-data, e,g, in the form of annotations like ratings. 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 eLearning(QA-EL)”.
- Prof. Dr. Iryna Gurevych
Former Research Staff
- Jürgen Steimle (GK “eLearning”)
- Markus Weimer (GK “eLearning”)