RUBRIC: A System for Rule-Based Information Retrieval
IEEE Transactions on Software Engineering - Special issue on COMPSAC 1982 and 1983
Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Notes and references on early automatic classification work
ACM SIGIR Forum
Using statistical testing in the evaluation of retrieval experiments
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
The effect of adding relevance information in a relevance feedback environment
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Foundations of statistical natural language processing
Foundations of statistical natural language processing
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Passage-Based Document Retrieval as a Tool for Text Mining with User's Information Needs
DS '01 Proceedings of the 4th International Conference on Discovery Science
Contextual relevance feedback in web information retrieval
IIiX Proceedings of the 1st international conference on Information interaction in context
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Information Retrieval Systems have been studied in Computer Science for decades. The traditional ad-hoc task is to find all documents relevant for an ad-hoc given query but the accuracy of ad-hoc document retrieval systems has plateaued in recent years. At DFKI, we are working on so-called collaborative information retrieval (CIR) systems which unintrusively learn from their users search processes. In this paper, a new approach is presented called term-based concept learning (TCL) which learns conceptual description terms occurring in known queries. A new query is expanded term by term using the previously learned concepts. Experiments have shown that TCL and the combination with pseudo relevance feedback result in notable improvements in the retrieval effectiveness if measured the recall/precision in comparison to the standard vector space model and to the pseudo relevance feedback. This approach can be used to improve the retrieval of documents in Digital Libraries, in Document Management Systems, in the WWW etc.