The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Model-based feedback in the language modeling approach to information retrieval
Abstract
The language modeling approach to retrieval has been shown to perform well empirically. One advantage of this new approach is its statistical foundations. However, feedback, as one important component in a retrieval system, has only been dealt with heuristically in this new retrieval approach: the original query is usually literally expanded by adding additional terms to it. Such expansion-based feedback creates an inconsistent interpretation of the original and the expanded query. In this paper, we present a more principled approach to feedback in the language modeling approach. Specifically, we treat feedback as updating the query language model based on the extra evidence carried by the feedback documents. Such a model-based feedback strategy easily fits into an extension of the language modeling approach. We propose and evaluate two different approaches to updating a query language model based on feedback documents, one based on a generative probabilistic model of feedback documents and one based on minimization of the KL-divergence over feedback documents. Experiment results show that both approaches are effective and outperform the Rocchio feedback approach.
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Cites
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Ponte, J. M., & Croft, W. B. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130368 · Cited by 2,503
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A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval
Zhai, C., & Lafferty, J. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130377 · Cited by 1,537
Self-citationRelevance-Based Language Models
Lavrenko, V., & Croft, W. B. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130376 · Cited by 1,422
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Cited by
A Survey of Automatic Query Expansion in Information Retrieval
Carpineto, C., & Romano, G. (2012) · ACM Computing Surveys · doi:10.1145/2071389.2071390 · Cited by 1,109
Yi, J., Nasukawa, T., Bunescu, R., & Niblack, W. (2004) · Third IEEE International Conference on Data Mining · doi:10.1109/icdm.2003.1250949 · Cited by 722
Query expansion techniques for information retrieval: a survey
Azad, H. K., & Deepak, A. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.05.009 · Cited by 313
Estimation methods for ranking recent information
Efron, M., & Golovchinsky, G. (2011) · Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval · doi:10.1145/2009916.2009984 · Cited by 78
A reinforcement learning framework for relevance feedback
Montazeralghaem, A., Zamani, H., & Allan, J. (2020) · Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3397271.3401099 · Cited by 36
Metadata
- Authors
- Zhai, C., & Lafferty, J.
- Venue
- Proceedings of the tenth international conference on Information and knowledge management
- Publisher
- ACM
- Published
- 2001-10-05
- Pages
- 403-410
- OpenAlex
- W1964348731
- Cited by
- 787
- References
- 24
- FWCI
- 7.48
- Type
- conference paper
- Language
- en
- Open access
- closed
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