The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A Markov random field model for term dependencies
Abstract
This paper develops a general, formal framework for modeling term dependencies via Markov random fields. The model allows for arbitrary text features to be incorporated as evidence. In particular, we make use of features based on occurrences of single terms, ordered phrases, and unordered phrases. We explore full independence, sequential dependence, and full dependence variants of the model. A novel approach is developed to train the model that directly maximizes the mean average precision rather than maximizing the likelihood of the training data. Ad hoc retrieval experiments are presented on several newswire and web collections, including the GOV2 collection used at the TREC 2004 Terabyte Track. The results show significant improvements are possible by modeling dependencies, especially on the larger web collections.
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Cites
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
Self-citationTHE PROBABILITY RANKING PRINCIPLE IN IR
ROBERTSON, S. E. (1977) · Journal of Documentation · doi:10.1108/eb026647 · Cited by 1,103
Cited by
The probabilistic relevance framework: BM25 and beyond
Robertson, S., & Zaragoza, H. (2009) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000019 · Cited by 3,374
Learning diverse rankings with multi-armed bandits
Radlinski, F., Kleinberg, R., & Joachims, T. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390255 · Cited by 503
Learning to Match using Local and Distributed Representations of Text for Web Search
Mitra, B., Diaz, F., & Craswell, N. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052579 · Cited by 426
Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search
Dai, Z., Xiong, C., Callan, J., & Liu, Z. (2018) · Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining · doi:10.1145/3159652.3159659 · Cited by 341
An Introduction to Neural Information Retrieval
Mitra, B., & Craswell, N. (2018) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000061 · Cited by 317
Metadata
- Authors
- Metzler, D., & Croft, W. B.
- Venue
- Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2005-08-15
- Pages
- 472-479
- OpenAlex
- W2070740689
- Cited by
- 843
- References
- 2
- FWCI
- 24.02
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Metzler, D., & Croft, W. B. (2005). A Markov random field model for term dependencies. Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/1076034.1076115
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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.
