The Information Retrieval Lab at the University of Delaware participated in the Relevance Feedback track at TREC 2009. We used only the Category B subset of the ClueWeb collection; our preprocessing and indexing steps are described in our paper on ad hoc and diversity runs [10]. The second year of the Relevance Feedback track focused on selection of documents for feedback. Our hypothesis is tha...

In this paper, we report on our TREC experiments with the ClueWeb09 document collection. We participated in the relevance feedback and web tracks. While our phase 1 relevance feedback run’s performance was good, our other relevance feedback and web track submissions’ performances were lacking. We suspect this performance difference is caused by the Category B document subset of the ClueWeb09 co...

Our research consortium develops user modeling methods for proactive applications. In this project we use machine learning methods for predicting users’ preferences from implicit relevance feedback. Our prototype application is information retrieval, where the feedback signal is measured from eye movements or user’s behavior. Relevance of a read text is extracted from the feedback signal with m...

In this paper, we report our experiments on the HARD (High Accuracy Retrieval from Documents) Track in TREC 2003. We focus on active feedback, i.e., how to intelligently propose questions for relevance feedback in order to maximize accuracy improvement in the second run. We proposed and empirically evaluated three different methods, i.e., top-k, gapped top-k, and k-cluster centroid, to extract ...

Dieser Beitrag beschreibt Retrievalexperimente mit einem umfangreichen multilingualen Korpus im Rahmen von WebCLEF 2006 an der Univer-sität Hildesheim. Im Vordergrund stand die Nut-zung von HTML Strukturelementen, der Einsatz von Blind Relevance Feedback und die Evaluie-rung des sprachunabhängigen Indexierungsansat-zes.

Relevance feedback methods have been used in information retrieval to generate improved query formulations based on information contained in previously retrieved documents. The relevance feedback techniques have been applied to extended Boolean query formulations as well as to vector query formulations. In this paper, we propose an adaptive way to improve the retrieval performance in an extende...

Same as Report (SAR) 18. NUMBER