Abstract
Relevance feedback (RF) has been an effective query modification approach to improving the performance of information retrieval (IR) by interactively asking a user whether a set of documents are relevant or not to a given query concept. The conventional RF algorithms either converge slowly or cost a user’s additional efforts in reading irrelevant documents. This paper surveys several RF algorithms and introduces a novel hybrid RF approach using a support vector machine (HRFSVM), which actively selects the uncertain documents as well as the most relevant ones on which to ask users for feedback. It can efficiently rank documents in a natural way for user browsing. We conduct experiments on Reuters-21578 dataset and track the precision as a function of feedback iterations. Experimental results have shown that HRFSVM significantly outperforms two other RF algorithms.
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References
C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, (2):121–167, 1998.
D. Cohn and Z. Ghahramani. Active learning with statistical models. Journal of Artificial Intelligence Research, (4):129–145, 1996.
H. Drucker, B. Shahraray, and D. Gibbon. Relevance feedback using support vector machines. In Proceedings of the 18th International Conference on Machine Learning, pages 122–129, 2001.
S. Dumais, J. Platt, D. Heckerman, and M. Sahami. Inductive learning algorithms and representations for text categorization. In Proceedings of the Seventh International Conference on Information and Knowledge Management. ACM Press, 1998.
D. Harman. Relevance feedback revisited. In Proceedings of the Fifth International SIGIR Conference on Research and Development in Information Retrieval, pages 1–10, 1992.
T. Joachims. Text categorization with support vector machines. In Proceedings of the European Conference on Machine Learning. Springer Verlag, 1998.
D. Lewis and W. Gale. A sequential algorithm for training text classifiers. In Proceedings of the Eleventh International Conference on Machine Learning, pages 148–156. Morgan Kaufmann, 1994.
T. Mitchell. Generalization as search. Artificial Intelligence, (28):203–226, 1982.
J. J. Rocchio. Relevance feedback in information retrieval. In The SMART Retrieval System: Experiments in Automatic Document Processing, pages 313–323. Prentice Hall, 1971.
G. Salton and C. Buckley. Improving retrieval performance by relevance feedback. Journal of the American Society of Information Science, 41:288–297, 1990.
G. Schohn and D. Cohn. Less is more: Active learning with support vector machines. In Proceedings of the Seventeenth International Conference on Machine Learning, 2000.
S. Tong and D. Koller. Support vector machine active learning with applications to text classification. Journal of Machine Learning Research, (2):45–66, 2001.
V. Vapnik. Estimation of Dependences Based on Empirical Data. Springer Verlag, 1982.
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Xu, Z., Xu, X., Yu, K., Tresp, V. (2003). A Hybrid Relevance-Feedback Approach to Text Retrieval. In: Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2003. Lecture Notes in Computer Science, vol 2633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36618-0_20
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DOI: https://doi.org/10.1007/3-540-36618-0_20
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