Zhai, C. and Lafferty, J. (2001). Document language models, query models, and risk minimization for information retrieval. SIGIR Conference on Research and Development in Information Retrieval (W. Croft, D. Harper, D. Home/Search Document Not in Database Summary Related Articles Check |
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CIRQUID: Complex Information Retrieval QUeries In a.. - Hiemstra, de Vries.. (Correct)
....(see e.g. 47] As such, they have really contributed to our understanding of content based retrieval. Because of the basic understanding of the underlying algorithms, the models are easily extended to support multiple query representations [2, 28, 64] and multiple content representations [41, 1, 35, 36], which makes them the ideal candidate for developing general purpose primitives for the combined querying of content and structure. As has been observed, most previous attempts at integrating database technology and information retrieval techniques have followed the approach of extending the ....
J. La#erty and C. Zhai. Document language models, query models, and risk minimization. In [13], pages 111--119, 2001.
Passage Retrieval Based On Language Models - Liu, Croft (2002) (1 citation) (Correct)
....been the subject of passage retrieval research. Different passage types include structural [4, 7] semantic [6, 14, 18] window based [4, 22] and arbitrary [8, 9] Recently, new retrieval approaches using generative models of documents and queries ( language models ) have been introduced to IR [15, 13, 19, 2, 10, 11]. This approach has shown promise as a formal framework for describing a range of retrieval processes, such as query expansion and eross lingual retrieval, and has produced excellent results using evaluation testbeds such as TREC. Given that the research on language modeling has been entirely ....
....determine the relevance of a document to a query, their model estimates the probability that the query would have been generated as a translation of that document. Documents are then ranked according to these probabilities. One notable feature of this model is an inherent query expansion component [11, 10]. However, there are also difficulties with application of this model: the need of a large collection of training data for translation probabilities, and inefficiency for ranking documents [10, 11] Lafferty and Zhai [10] proposed a new framework that extends the existing language modeling ....
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Lafferty, J. and Zhai, C. (2001). Document language models, query models, and risk minimization for information retrieval. In W.B. Croft, D.J. Harper, D.H. Kraft, & J. Zobel (Eds.), Proceedings of the 24 tn annual international ACM-SIGIR conference on research and development in information retrieval, New Orleans, Louisiana (pp.111-119), New York: ACM.
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....Finally, we conclude in Section 6. 2 n Gram Language Modeling Traditionally, the dominant motivation for language modeling has come from speech recognition. However statistical language models have recently become more widely used in many other application areas, including information retrieval [8, 10, 12], text classi cation [11] and now we are applying it for Web mining in this paper. The goal of language modeling is to predict the probability of natural word sequences, or more simply, to put high probability on word sequences that actually occur (and low probability on word sequences that ....
La erty, J. and Zhai, C.; (2001). Document Language Models, Query Models, and Risk Minimization for Information Retrieval. In Proceedings of 24th ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR).
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Lafferty, J. and Zhai, C. (2001). Document language models, query models, and risk minimization for information retrieval. In Proceedings of SIGIR'01, pages 111--119.
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Lafferty, J. and Zhai, C. (2001). Document language models, query models, and risk minimization for information retrieval. In Proceedings of SIGIR'2001, pages 111--119.
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Zhai, C. and Lafferty, J. (2001). Document language models, query models, and risk minimization for information retrieval. SIGIR Conference on Research and Development in Information Retrieval (W. Croft, D. Harper, D.
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La#erty, J., & Zhai, C. (2001). Document language models, query models, and risk minimization for information retrieval. Proceedings of the 24th ACM SIGIR Conference.
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J. Lafferty, C. Zhai. 2001. Document Language Model, Query Models and Risk Minimization for Information Retrieval. SIGIR01
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La#erty, J. & Zhai, J. (2001). Document language models, query models and risk minimization for information retrieval. In 24th annual international ACM SIGIR conference, (pp. 111--119), New Orleans, Louisiana.
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J. La#erty and C. Zhai. Document language models, query models, and risk minimization. In Proceedings of the 24th ACM Conference on Research and pages 111--119, 2001.
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Lafferty, J., and C. Zhai. Document language models, query models, and risk minimization for information ACM SIGIR Conference on Research and Development in Information Retrieval (2001), ACM Press, 111-119.
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Lafferty, J and Zhai, C. (2001) Document Language Models, Query Models, and Risk Minimization for Information Retrieval. In Proceedings of the 24th Annual International Conference on Research and Development in Information Retrieval (SIGIR'01), pp. 111-119.
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J. Lafferty, C. Zhai. 2001. Document Language Model, Query Models and Risk Minimization for Information Retrieval. SIGIR01
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J. La#erty and C. Zhai. Document language models, query models, and risk minimization. In Proceedings of the 24th ACM Conference on Research and pages 111--119, 2001.
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Lafferty, J and Zhai, C. (2001) Document Language Models, Query Models, and Risk Minimization for Information Retrieval. In Proceedings of the 24th Annual International Conference on Research and Development in Information Retrieval (SIGIR'01), pp. 111-119.
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