Abstract
For our first participation in CLEF we chose the domain specific GIRT corpus. We implemented the adaptive fusion model MIMOR (Multiple Indexing and Method-Object Relations) which is based on relevance feedback. The linear combination of several retrieval engines was optimized. As a basic retrieval engine, IRF from NIST was employed. The results are promising. For several topics, our runs achieved a performance above the average. The optimization based on topics and relevance judgements from CLEF 2001 proved to be a fruitful strategy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Mandl, T., Womser-Hacker, C.: Probability Based Clustering for Document and User Properties. In: Ojala, T. (ed.): Infotech Oulo International Workshop on Information Retrieval (IR 2001). Oulo, Finland. Sept 19–21 2001. 100-107
Womser-Hacker, C.: Das MIMOR-Modell. Mehrfachindexierung zur dynamischen Methoden-Objekt-Relationierung im Information Retrieval. Habilitationsschrift. Universität Regensburg, Informationswissenschaft (1997)
McCabe, M., Chowdhury, A., Grossmann, D., Frieder, O.: A Unified Framework for Fusion of Information Retrieval Approaches. In: Eigth ACM Conference on Information and Knowledge Management (CIKM) ACM Press, New York, NY, USA (1999) 330–334
Mandl, T., Womser-Hacker, C.: Fusion Approaches for Mappings Between Heterogeneous Ontologies. In: Constantopoulos, Panos; Sølvberg, Ingeborg (eds.): Research and Advanced Technology for Digital Libraries: 5th European Conference (ECDL) Lecture Notes in Computer Science, Vol. 2163 Springer-Verlag, Berlin Heidelberg New York (2001) 83–94
Vogt, C., Cottrell, G.: Predicting the Performance of Linearly Combined IR Systems. In: 21th Annual Intl ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)) ACM Press, New York, NY, USA (1998) 190–196
Drucker, H., Wu, D., Vapnik, V.: Support Vector Machines for Spam Categorization. IEEE Trans. on Neural Networks 10 (1999) 1048–1054
Joachims, T.: Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In: European Conference on Machine Learning (ECML 1998) 137-142
Iyer, R., Lewis, D., Schapire, R., Singer, Y., Singhal, A.: Boosting for Document Routing. In: Ninth ACM Conference on Information and Knowledge Management (CIKM) ACM Press, New York, NY, USA (2000) 70–77
Kluck, M., Gey, F.: The Domain-Specific Task of CLEF — Specific Evaluation Strategies in Cross-Language Information Retrieval. In: Peters, Carol (ed.): Cross-Language Information Retrieval and Evaluation. Workshop of the Cross-Language Information Evaluation Forum (CLEF) Lecture Notes in Computer Science, Vol. 2069 Springer-Verlag, Berlin Heidelberg New York (2000) 48–56
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hackl, R., Kölle, R., Mandl, T., Womser-Hacker, C. (2003). Domain Specific Retrieval Experiments with MIMOR at the University of Hildesheim. In: Peters, C., Braschler, M., Gonzalo, J., Kluck, M. (eds) Advances in Cross-Language Information Retrieval. CLEF 2002. Lecture Notes in Computer Science, vol 2785. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45237-9_30
Download citation
DOI: https://doi.org/10.1007/978-3-540-45237-9_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40830-7
Online ISBN: 978-3-540-45237-9
eBook Packages: Springer Book Archive