The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
A hidden Markov model information retrieval system
Open accessAbstract
We p r e s e n t a new method for information retrieval using hidden Markov models (HMMs).We d e v elop a general framework for incorporating multiple word generation mechanisms within the same model.We then demonstrate that an extremely simple realization of this model substantially outperforms standard tf :idf ranking on both the TREC-6 and TREC-7 ad hoc retrieval tasks.We go on to present a novel method for performing blind feedback in the HMM framework, a more complex HMM that models bigram production, and several other algorithmic re nements.Together, these methods form a state-of-the-art retrieval system that ranked among the best on the TREC-7 ad hoc retrieval task.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
An algorithm for suffix stripping
Porter, M. F. (1980) · Program · doi:10.1108/eb046814 · Cited by 8,096
Relevance feedback in information retrieval
Rocchio, J. J. (1971) · Medical Entomology and Zoology · Cited by 2,680
A Language Modeling Approach to Information Retrieval
Ponte, J. M., & Croft, W. B. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130368 · Cited by 2,503
The SMART Retrieval System—Experiments in Automatic Document Processing
Salton, G. (1971) · CERN Document Server (European Organization for Nuclear Research) · Cited by 2,252
Robertson, S. E., Walker, S., Jones, S., Hancock‐Beaulieu, M., & Gatford, M. (1994) · Text REtrieval Conference · Cited by 2,212
Relevance weighting of search terms
Robertson, S. E., & Jones, K. S. (1976) · Journal of the American Society for Information Science · doi:10.1002/asi.4630270302 · Cited by 2,081
A language modeling approach to information retrieval
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
On Relevance, Probabilistic Indexing and Information Retrieval
Maron, M. E., & Kuhns, J. L. (1960) · Journal of the ACM · doi:10.1145/321033.321035 · Cited by 907
Cited by
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
A study of smoothing methods for language models applied to information retrieval
Zhai, C., & Lafferty, J. (2004) · ACM Transactions on Information Systems · doi:10.1145/984321.984322 · Cited by 1,201
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Zhai, C., & Lafferty, J. (2001) · Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/383952.384019 · Cited by 889
Model-based feedback in the language modeling approach to information retrieval
Zhai, C., & Lafferty, J. (2001) · Proceedings of the tenth international conference on Information and knowledge management · doi:10.1145/502585.502654 · Cited by 787
Translation techniques in cross-language information retrieval
Zhou, D., Truran, M., Brailsford, T., Wade, V., & Ashman, H. (2012) · ACM Computing Surveys · doi:10.1145/2379776.2379777 · Cited by 698
Metadata
- Authors
- Miller, D. R. H., Leek, T., & Schwartz, R. M.
- Venue
- Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 1999-08
- Pages
- 214-221
- OpenAlex
- W2095368471
- Cited by
- 469
- References
- 18
- FWCI
- 24.93
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- CoChat. (2026). Citation check: A hidden Markov model information retrieval system. https://app.cochat.ai/papers/10.1145/312624.312680
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- Miller, D. R. H., Leek, T., & Schwartz, R. M. (1999). A hidden Markov model information retrieval system. Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/312624.312680
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
