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Azzopardi, L.; Girolami, M.; Van Rijsbergen, C. (2004)
Publisher: Institute of Electrical and Electronics Engineers
Languages: English
Types: Other
Subjects: QA75
We propose a topic based approach lo language\ud modelling for ad-hoc Information Retrieval (IR). Many smoothed estimators used for the multinomial query model in IR rely upon the estimated background collection probabilities. In this paper, we propose a topic based language modelling approach, that uses a more informative prior based on the topical content of a document. In our experiments, the proposed model provides comparable IR performance to the standard models, but when combined in a two stage language model, it outperforms all other estimated models.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [I] D. M. Blei. A. Y. Ng, and M. 1. Jordan. Latent dirichlet allocation. b u m l of Machine Leorning Research, 3:993-1022, 2003.
    • 121 D. Cohn and H.Chang. Learning to probabilistically identify autharilative documenu. In Proceedings of the 171h Interwriowl Conference on Machine Learning, pages 167-174. Morgan Kaufmann. 2oW.
    • [31 D. Cohn and T. Hofmann. The missing link A probabilistic model of document content and hypenext connectivity. In Advrvlccs in Neural lnfomwrion Pmccssing Sysremr (NlPS'l3). MIT Press. 2001.
    • 128 2 0: Model I k I X: 1 0.1 I 0.2 I 0.3 I 0.5 I 0.7 I 33.1 1 34.3 I 34.4 I 33.6 I 32.3 I 29.2 I 31.5 I 32.4 I 32.8 I 31.9 14.9 33.3 12.4 12.6 18.0 34.3 250 35.2 32.0 20.1 34.4 500 36.9 31.6 23.4 33.6 150 36.9 30.7 25.8 32.4 1000 36.3 29.9 M. Giralami and A. Kaban. On an equivalence between plsi and Ida. In 26th Annual ACM Conference on Research and Development in Informrrrion Rerrievnl. SIGIR, pages 433434, Toronto, Canada. 2003. T. Hofmann. Probabilirtc latent semantic indexing. In Proceedings of the Z Z n d Inremotional Confemnce on Research nnd Development in Information Retrieval. ACM Press. 1999.
    • [6] T. Hofmann. Unsupervised learning by probabilistic latent semantic analysis. Machine Learnins. 42:171-196. 2001.
    • 171 N. Jardine and C. J. Van Rijsbergen. The use of hierarchic clustering in information retrieval. Informarion Storagge and R e t n ' e ~ l 7,:211-240. ~1971~.
    • [SI D. R. H. Miller. T. Leek. and R. M. Schwanz. A hidden markov model information retrieval. In 22nd Annual Internotiom1 ACM SIGlR conference on Research and development in information rerrirvol,pages 21&221. California, US, 1999. ACM Press.
    • [91 J. M. Ponte and W. B. Croh. A language modeling approach to information retrieval. In Proceedings of the Twenty Firs1 ACM-SICIR. pages 275-281. Melboume. Australia, 1998. ACM Press.
    • [IO] F. Song and W. B. Croft A general language model for information retrieval. In SIGIR ACM Resenrch and Dev-elopmenr in Infomrion Retrieval, pages 219-280. Berkeley, CA., 1999.
    • Ill]K. Sparck-Jones, S. E. Robertson. D. Hiemsua, and H. Zarapora. Language modeling and relevance. In W. B. Croft and J. Laffetty. editon, Longusc Modeling for Informarion R e t r i ~ l p.ages 57-11. Kluwer Academic Publishen, 2W3.
    • [I21 H. Zaragoza, D. Hiemsua. M. lipping. and S. Robemon. Bayesian extension to the language model for ad hoc information retrieval. In Twenry-SixthAnnuol Internotional A C M SIGIR Confereme on Rescorrh and Development in Infomrion Retrieval. pages 4-9, Tamnto. Canada July 2003.
    • [I31 C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to ad ha: information retrieval. In ZWI ACM SIGlR Conference on Research and Development in I n f o m i o n Retrieval (SIGIR), pages 49-56. New Orleans, LO, 2001. ACM Press.
    • [I41 C. Zhai and J. Laffeny. Twostage language models for infromation retrieval. In 2002 ACM SIGIR Conference on Rescorch and D ~ ~ l o p t ~ t in Infomuuion Retrieval ( S I G R I . pages 49-56, Tampere. Finland, 2002
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