Tentative List of Topics:

02/21		Course Overview & Introduction	cf. Modern Information Retrieval, Ch. 1
03/07		Classical Models	cf. Modern Information Retrieval, Cha.3
03/14		Classical Models	Experimental Corpus
03/21		Evaluation Metrics	cf. Modern Information Retrieval, Ch. 4
03/28		Benchmark Collections	cf. Modern Information Retrieval, Ch. 4
04/04		Spring Break
04/11		Extensions of Classic (Set, Algebra & Probabilistic) Models	cf. Modern Information Retrieval, Ch. 3
04/18		Relevance Feedback and Query Expansion	cf. Modern Information Retrieval, Ch. 5
04/25		Relevance Feedback and Query Expansion	cf. Modern Information Retrieval, Ch. 5
05/02		Midterm
05/09		Latent Semantic Analysis User Interfaces for Search	cf. Modern Information Retrieval, Ch. 2 & 3
05/16		Language Models for Information Retrieval
05/16		Exercise (Language Modeling for IR)
05/30		Indexing and Searching	cf. Modern Information Retrieval, Ch. 9
06/06		Dragon Boat Festival
06/13		Indexing and Searching Web Search Basics	cf. Introduction to Information Ch 19-21

Textbooks: 

•		R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval: The Concepts and Technology behind Search (2nd Edition), ACM Press, 2011
•		Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008
•		W. Bruce Croft, Donald Metzler, and Trevor Strohman, Search Engines: Information Retrieval in Practice, Addison Wesley, 2009

References:
  Books:

•		C.X. Zhai, "Statistical Language Models for Information Retrieval (Synthesis Lectures Series on Human Language Technologies)," Morgan & Claypool Publishers, 2008)
•		W. B. Frakes and R. Baeza-Yates, Information Retrieval: Data Structures & Algorithms,  Prentice-Hall, 1992.
•		T. K. Landauer, D. S. McNamara, S. Dennis, W. Kintsch (eds.) , Handbook of Latent Semantic Analysis, Lawrence Erlbaum, 2007
•		D. A. Grossman, O. Frieder, Information Retrieval: Algorithms and Heuristics, Springer. 2004.
•		I. H. Witten, A. Moffat, and T. C. Bell, Managing Gigabytes: Compressing and Indexing Documents and Images, Morgan Kaufmann Publishing, 1999.
•		C. Manning and H. Schutze, Foundations of Statistical Natural Language Processing, MIT Press, 1999.
•		D. Jurafsky and J. H. Martin, Speech and Language Processing, Prentice-Hall, 2000.
•		W.B. Croft and J. Lafferty (eds.), Language Models for Information Retrieval, Kluwer International Series on Information Retrieval, Volume 13, Kluwer Academic Publishers, 2002.
•		Stephen Robertson and Hugo Zaragoza, The Probabilistic Relevance Framework: BM25 and Beyond. Foundations and Trends in Information Retrieval 3 no. 4, 333-389 (2009).

.

Papers:

•		O. Kolomiyets, M.-F. Moens, "A survey on question answering technology from an information retrieval perspective," Information Sciences 181 (2011) 5412–5434
•		D. Blei, A. Ng, and M. Jordan, "Latent Dirichlet allocation,"  Journal of Machine Learning Research, 3:993-1022, January 2003.
•		V. Lavrenko and W.B. Croft, "Relevance-Based Language Models"  ACM SIGIR 2001.
•		C. H. Papadimitriou, P. Raghavan, H. Tamaki, S. Vempala, "Latent semantic indexing: A probabilistic analysis,'' analyzes an information retrieval technique related to principle components analysis.
•		Liu, X. and Croft, W.B., "Statistical Language Modeling For Information Retrieval,"  the Annual Review of Information Science and Technology, vol. 39, 2005
•		Lan Huang. A Survey On Web Information Retrieval Technologies. 2000.
•		Karen Spa¨rck Jones, "Some Points in a Time," Computational Linguistics, Vol. 31, No. 1, 2005.
•		D. Hiemstra, "Information Retrieval Model," In: A. Goker, J. Davies, and M. Graham (eds.), Information Retrieval: Searching in the 21st Century, Wiley, 2009
•		M. Steyvers, T. Griffiths,  "Probabilistic Topic Models," In T. K. Landauer, D. S. McNamara, S. Dennis, W. Kintsch (eds.). Handbook of Latent Semantic Analysis, Mahwah NJ: Lawrence Erlbaum, 2007.
•		X. Yi, J. Allan,  "A Comparative Study of Utilizing Topic Models for Information Retrieval," in the Proceedings of ECIR'09.
•		Nallapati, Discriminative Models for Information Retrieval, in the Proceedings of SIGIR 2004
•		T. Joachims and F. Radlinski, Search Engines that Learn from Implicit Feedback, IEEE Trans. on Computer 40(8), pp. 34-40, 2007
•		B. Chen, H.M. Wang, L.S. Lee, “A discriminative HMM/N-gram-based retrieval approach for Mandarin spoken documents,” ACM Transactions on Asian Language Information Processing, Vol. 3, No. 2, pp. 128-145, June 2004.

　

Information Retrieval Resources

         •   SIGIR-Information Retrieval Resources