Frequentist and Bayesian Approach to Information Retrieval

Author:

Amati Giambattista

Publisher

Springer Berlin Heidelberg

Reference27 articles.

1. Amati, G.: Probability Models for Information Retrieval based on Divergence from Randomness. PhD thesis, University of Glasgow (June 2003)

2. Amati, G., Carpineto, C., Romano, G.: FUB at TREC 10 web track: a probabilistic framework for topic relevance term weighting. In: Voorhees, E., Harman, D. (eds.) Proceedings of the 10th Text Retrieval Conference TREC 2001, Gaithersburg, MD, pp. 182–191. NIST Special Publication 500-250 (2002)

3. Amati, G., Carpineto, C., Romano, G.: Fondazione Ugo Bordoni at TREC 2004. In: Voorhees, E., Harman, D. (eds.) Proceedings of the 13th Text Retrieval Conference TREC 2001, Gaithersburg, MD, NIST Special Publication 500-261 (2004)

4. Amati, G., Van Rijsbergen, C.J.: Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Transactions on Information Systems (TOIS) 20(4), 357–389 (2002)

5. Bahl, L.R., Jelinek, F., Mercer, R.L.: A maximum likelihood approach to continuous speech recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-5 2, 179–190 (1983)

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