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USING PROBABILISTIC MODELS OF DOCUMENT RETRIEVAL WITHOUT RELEVANCE INFORMATION
Abstract
Most probabilistic retrieval models incorporate information about the occurrence of index terms in relevant and non‐relevant documents. In this paper we consider the situation where no relevance information is available, that is, at the start of the search. Based on a probabilistic model, strategies are proposed for the initial search and an intermediate search. Retrieval experiments with the Cranfield collection of 1,400 documents show that this initial search strategy is better than conventional search strategies both in terms of retrieval effectiveness and in terms of the number of queries that retrieve relevant documents. The intermediate search is shown to be a useful substitute for a relevance feedback search. Experiments with queries that do not retrieve relevant documents at high rank positions indicate that a cluster search would be an effective alternative strategy.
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Cited by
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Robertson, S., & Zaragoza, H. (2009) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000019 · Cited by 3,374
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Ponte, J. M., & Croft, W. B. (1998) · Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/290941.291008 · Cited by 1,821
Self-citationAn information-theoretic perspective of tf–idf measures
Aizawa, A. (2003) · Information Processing & Management · doi:10.1016/s0306-4573(02)00021-3 · Cited by 1,419
A Sequential Algorithm for Training Text Classifiers
Lewis, D. D., & Gale, W. A. (1994) · SIGIR ’94 · doi:10.1007/978-1-4471-2099-5_1 · Cited by 1,208
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Olteanu, A., Castillo, C., Diaz, F., & Vieweg, S. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14538 · Cited by 397
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Azad, H. K., & Deepak, A. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.05.009 · Cited by 313
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
Xiong, W., Li, X. L., Iyer, S., Du, J., Lewis, P., Wang, W. Y., Mehdad, Y., Yih, W.-T., Riedel, S., Kiela, D., et al. (2020) · arXiv · doi:10.48550/arxiv.2009.12756 · Cited by 112
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
- CROFT, W. B., & HARPER, D. J.
- Venue
- Journal of Documentation
- Publisher
- Emerald
- Published
- 1979-04-01
- Volume
- 35
- Issue
- 4
- Pages
- 285-295
- ISSN
- 0022-0418 (ISSN-L)
- OpenAlex
- W2102046030
- Cited by
- 433
- References
- 0
- FWCI
- 29.00
- Type
- journal article
- Language
- en
- Open access
- closed
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
