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Classifying and filtering blind feedback terms to improve information retrieval effectiveness

Leveling, Johannes orcid logoORCID: 0000-0003-0603-4191 and Jones, Gareth J.F. orcid logoORCID: 0000-0003-2923-8365 (2010) Classifying and filtering blind feedback terms to improve information retrieval effectiveness. In: RIAO 2010 - 9th RIAO Conference, 28-30 April, 2010, Paris, France.

The classification of blind relevance feedback (BRF) terms described in this paper aims at increasing precision or recall by determining which terms decrease, increase or do not change the corresponding information retrieval (IR) performance metric. Classification and IR experiments are performed on the German and English GIRT data, using the BM25 retrieval model. Several basic memory-based classifiers are trained on dierent feature sets, grouping together features from different query expansion (QE) approaches. Combined classifiers employ the results of the basic classifiers and correctness predictions as features. The best combined classifiers for German (English) yield 22.9% (26.4%) and 5.8% (1.9%) improvement for term classification wrt.precision and recall compared to the best basic classifiers. IR experiments based on this term classification have also been performed. Filtering out different types of BRF terms shows that selecting feedback terms predicted to increase precision improves the average precision significantly compared to experiments without BRF. MAP is improved by +19.8% compared to the best standard BRF experiment (+11% for German). BRF term classification also increases the number of relevant and retrieved documents, geometric MAP, and P@10 in comparison to standard BRF. Experiments based on an optimal classification show that there is potential for improving IR effectiveness even more.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Subjects:Computer Science > Information retrieval
DCU Faculties and Centres:Research Institutes and Centres > Centre for Next Generation Localisation (CNGL)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Official URL:http://riao2010.org
Copyright Information:Copyright © 2010 Centre de Hautes Etudes Internationales d'Informatique Documentaire (CID)
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland
ID Code:15839
Deposited On:25 Nov 2010 12:08 by Shane Harper . Last Modified 25 Oct 2018 10:40

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