Leveraging User Interaction and Social Tagging for Improving Cross-lingual Information Access in Digital Libraries

Evaluation of interactive cross-lingual information retrieval systems has been the focus of recent research. The goal is to support the users in formulating effective queries and selecting the documents which satisfy their information needs regardless of the language of the documents. This dissertation aims at harnessing the user-system interaction, extracting the added value and integrating it back into the system to improve the cross-lingual information retrieval system for successive users. To achieve this, user input at different interaction points will be evaluated. This will among others include interaction during user-assisted query translations, implicit and explicit relevance feedback and social tags. To leverage this input, explorative studies need to be conducted to establish user input which might be beneficial and the methods to extract it. The dissertation wants to extend the scope of interactive cross-lingual information retrieval by harnessing user input as a mean for improving cross-lingual information retrieval tools.