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Bibliografía de Referencia sobre RI

Tabla de Contenidos

Listado de referencias bibliográficas, en formato APA y ordenadas alfabéticamente por autor.

A

  • Adar E. (2007). User 4xxxxx9: Anonymizing query logs. Query Logs. Workshop of the International Conference on World Wide Web.
  • Adomavicius, G. & Tuzhilin, A. (2005). Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), pp. 734-749.
  • Aguirre, E., Alfonseca, E., Hall, K., Kravalova, J., Pasca, M. & Soroa, A. (2009). A Study on Similarity and Relatedness Using Distributional and WordNet-based Approaches, Proceedings of NAACL-HLT-2009.
  • Aguirre, E., Ansa, O., Hovy, E. & Martinez, D. (2000). Enriching very large ontologies using the www. Proceedings of ECAI-2000, Workshop on Ontology Learning.
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  • Alexander, J. & Green, S. (2008). The Minion Search Engine: Indexing, Search, Text Similarity and Tag Gardening. En JavaOne Conference. En http://dsc.sun.com/learning/javaoneonline/2008/pdf/TS-5027.pdf.
  • Allan, J., Carbonell, J., Doddington, G., Yamron, J. & Yang, Y. Topic Detection and Tracking Pilot Study: Final Report (1998). En Proceedings of DARPA Broadcast News Transcription and Understanding Workshop. Morgan Kaufmann, pp. 194-218.
  • Allan, J., Wade, C. & Bolivar, A. (2003). Retrieval and novelty detection at the sentence level. En Proceedings of the 26th ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR 2003. ACM, New York, NY, pp. 314-321.
  • Amati, G., Ambrosi, E., Bianchi, M., Gaibisso, C. & Gambosi, G. (2008). Automatic construction of an opinion-term vocabulary for ad hoc retrieval. En Proceedings of the 30th European Conference on IR Research on Advances in Information Retrieval. ECIR 2008, pp. 89-100.
  • Amati, G. & van Rijsbergen, C. J. (2002). Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Trans. Inf. Syst. 20(4): 357-389.
  • Anagnostopoulos, A., Broder, A., Gabrilovich, E., Josifovski, V. & Riedel, L. (2007). Just-in-Time Contextual Advertising. En Proceeding of the 16th ACM Conference on Information and Knowledge Management. CIKM 2007.
  • Arasu, A., Cho, J., Garcia-Molina, H., Paepcke, A. & Raghavan, S. (2001). Searching the web. ACM Transactions on Internet Technology (TOIT), 1(1), pp. 2-43.
  • Ares, M. E., Parapar, J. & Barreiro, A. (2009). Avoiding Bias in Text Clustering Using Constrained K-means and May-Not-Links. En Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory. Lecture Notes in Computer Science. Springer-Verlag, pp. 322-329.
  • Arguello, J., Elsas, J., Callan, J. & Carbonell, J. (2008). Document representation and query expansion models for blog recommendation. En Proceedings of the International Conference on Weblogs and Social Media (ICWSM 2007). AAAI.
  • Armstrong, T. G., Moffat, A., Webber, W. & Zobel, J. (2009). Has adhoc retrieval improved since 1994? En Proceedings of the 32nd ACM SIGIR International Conference on Research and Development in Information Retrieval. SIGIR 2009. ACM, New York, NY, pp. 692-693.

B

  • Bae, E. & Bailey, J. (2006). COALA: A Novel Approach for the Extraction of an Alternate Clustering of High Quality and High Dissimilarity. En Proceedings of the Sixth International Conference on Data Mining. ICDM 2006. IEEE Computer Society, Washington, DC, pp. 53-62.
  • Baeza-Yates, R. A., Castillo, C., Junqueira, F., Plachouras, V. & Silvestri, F. (2007). Challenges on distributed web retrieval. En International Conference on Data Engineering, ICDE 2007, pp. 6-20.
  • Baeza-Yates, R. A., Gionis, A., Junqueira, F., Murdock, V., Plachouras, V. & Silvestri, F. (2008). Design trade-offs for search engine caching. ACM Transactions on the Web (TWEB), 2(4).
  • Baeza-Yates, R. A. & Ribeiro-Neto, B. (1999). Modern Information Retrieval. Addison Wesley.
  • Baeza-Yates, R. A. & Ribeiro-Neto, B. (2011). Modern Information Retrieval: The Concepts and Technology behind Search (2nd Edition). Addison-Wesley.
  • Baeza-Yates, R., Saint-Jean, F. & Castillo, C. (2002). Web structure, dynamics, and page quality. Proceedings of the International Symposium on String Processing and Information Retrieval, 117-130.
  • Bailey, P., Craswell, N., de Vries, A. P. & Soboroff, I. (2007). Overview of the TREC-2007 Enterprise track. En Proceedings of the 16th Text REtrieval Conference, TREC 2007.
  • Basu, S., Bilenko, M. & Mooney, R. J. (2004). A probabilistic framework for semi-supervised clustering. En Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2004. ACM, New York, NY, pp. 59-68.
  • Bates, M. J. (1989). The design of browsing and berrypicking techniques for the online search interface. Online Review, 13, pp. 407-424.
  • Bates, M. J. (2010). Information Behavior. En Encyclopedia of Library and Information Sciences. M. J. Bates y M. N. Maack (Eds). New York: CRC Press, vol. 3, pp. 2381-2391.
  • Beckett, D. (ed.) (2004). RDF/XML Syntax Specification (Revised). En http://www.w3.org/TR/rdf-syntax-grammar (consultado 13 enero 2011).
  • Beeferman, D. & Berger, A. (2000). Agglomerative Clustering of a search engine query log. International Conference on Knowledge Discovery and Data Mining 2000.
  • Beeferman, D., Berger, A. & Lafferty, J. (1999). Statistical Models for Text Segmentation. Machine Learning, 34(1-3), pp. 177-210.
  • Belkin, N. J, Cool, C., Kelly, D., Lin, S. J., Park, S.Y., Pérez-Carballo, J. & Sikora, C. (2001). Iterative exploration, design and evaluation of support for query reformulations in interactive information retrieval. Information Processing & Management, 37(3), pp. 403-434.
  • Belkin, N. J., Oddy, R. N. & Brooks, H. M. (1982). ASK for information retrieval: Part I. Background and theory. Journal of Documentation, 38(2), pp. 61-71.
  • Bell, R., Bennett, J., Koren, Y. & Volinsky, C. (2009). The million dollar programming prize. IEEE Spectrum 46(5), pp. 28-33.
  • Berlin, B. & Kay, P. (1969). Basic Color Terms: Their Universality and Evolution.
  • Berners-Lee, T. (1989). Information Management: A Proposal. Technical Report, CERN.
  • Berners-Lee, T. (2000). Semantic Web - XML2000. En http://www.w3.org/2000/Talks/1206-xml2k-tbl/ (consultado 13 diciembre 2010).
  • Berners-Lee, T., Cailliau, R., Luotonen, A., Nielsen, H. F., Secret, A. (1994). The World-Wide Web. Communication of the ACM, 37(8), pp. 76-82.
  • Berners-Lee, T., Hendler, J. & Lassila, O. (2001). The Semantic Web: A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. The Scientific American 284(5), pp. 35-43.
  • Bishop, C., (2006). Pattern Recognition and Machine Learning. Berlin: Springer.
  • Blair, D. C. & Maron, M. E. (1985). An evaluation of retrieval effectiveness for a full-text document-retrieval system. Communications of the ACM, 28, pp. 289-299.
  • Blanco, R. & Barreiro, A. (2006). TSP and cluster-based solutions to the reassignment of document identifiers. Information Retrieval. 9, 4, pp. 499-517.
  • Blandford, D. & Blelloch, G. (2002). Index Compression through Document Reordering. En Proceedings of the Data Compression Conference. DCC. IEEE Computer Society.
  • Borgman, C. L. (2003). From Gutenberg to the global information infrastructure: access to information in the networked world. MIT Press.
  • Boydell, O. & Smyth, B., (2006). Capturing community search expertise for personalized web search using snippet-indexes. En Proceeding of the ACM Conference on Information and Knowledge Management. CIKM 2006.
  • Breese, J. S., Heckerman, D. & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. En Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence. UAI 1998, pp. 43-52.
  • Broder, A. (2002). A taxonomy of web search. SIGIR Forum, 36(2), pp. 3-10.
  • Broder, A., Ciaramita, M., Fontoura, M., Gabrilovich, E., Josifovski, V., Metzler, D., Murdock, V. & Plachouras, V. (2008). To swing or not to swing: learning when (not) to advertise. En Proceedings of the 17th ACM Conference on Information and Knowledge Management. CIKM 2008. ACM, pp. 1003-1012.
  • Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., Tomkins, A. & Wiener, J. (2000). Graph structure in the Web. Computer networks, 33(1-6), pp. 309-320.
  • Buckley, C. & Robertson, S. (2008). Relevance Feedback Track Overview: TREC 2008. En Proceedings of the 17th Text REtrieval Conference, TREC 2008.
  • Burges, C. J., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N. & Hullender, G. (2005). Learning to rank using gradient descent. En International Conference on Machine Learning, ICML 2005, pp. 89-96.
  • Bush, V. (1945). As we may think. The Atlantic Monthly, 176(1), pp. 101-108.

C

  • Cacheda, F., Plachouras, V. & Ounis, I. (2005). A case study of distributed information retrieval architectures to index one terabyte of text. Information. Processing & Management, 41 (5), pp. 1141-1161.
  • Cafarella, M. J. & Cutting, D. (2004). Building Nutch: Open Source Search. ACM Queue 2, 2, pp. 54-61.
  • Cafarella, M. J, Halevy, A., Zhang, Y., Wang, D. Z. & Wu, E. WebTables: Exploring the Power of Tables on the Web. Proceedings of International Conference on Very Large Data Bases.VLDB 2008.
  • Califf, M (2003). Bottom-up relational learning of pattern matching rules for information extraction. Journal of Machine Learning Research, 4, pp. 177-210.
  • Callan, J. (2000). Distributed information retrieval. En W. B. Croft, editor, Advances in Information Retrieval, chapter 5. Kluwer Academic Publishers, pp. 127-150.
  • Candela, L., Castelli, D., Ferro, N., Ioannidis, Y., Koutrika, G., Meghini, C., Pagano, P., Ross, S., Soergel, D., Agosti, M., Dobreva, M., Katifori, V. & Schuldt, H. (2010). The Digital Library Reference Model v1. Disponible en: http://www.dlorg.eu/uploads/DL%20Reference%20Models/D3.2aTheDigitalLibrary\ ReferenceModel_web.pdf (visitado el 4 de octubre de 2010).
  • Candela, L., Castelli D., Pagano, P., Thanos, C., Ioannidis, Y., Koutrika, G., Ross, S., Schek, H. & Schuldt, H. (2007). Setting the Foundations of Digital Libraries. The DELOS Manifesto, D-Lib Magazine, 13(3-4).
  • Carbonell, J. & Goldstein, J. (1998). The use of MMR, diversity-based reranking for reordering documents and producing summaries. En Proceedings of the 21st ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR 1998. ACM, New York, NY, pp. 335-336.
  • Carterette, B. & Chandar, P. (2009). Probabilistic models of novel document rankings for faceted topic retrieval. En Proceedings of the 18th ACM Conference on Information and Knowledge Management. CIKM 2009. ACM, pp. 1287-1296.
  • Carterette, B., Fang, H., Pavlu, V. & Kanoulas, E. (2009). Million Query Track 2009 Overview. En Proceedings of the 18th Text REtrieval Conference, TREC 2009.
  • Case, D. O. (2008). Looking for Information: A Survey of Research on Information Seeking, Needs, and Behavior. Bingley: Emerald.
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  • Chang, F., Dean, J., Ghemawat, S., Hsieh, W., Wallach, D. A., Burrows, M., Chandra, T., Fikes, A. & Gruber, R. E. (2006). Bigtable: A Distributed Storage System for Structured Data. En Seventh Symposium on Operating System Design and Implementation, OSDI 2006.
  • Chen, H. & Karger, D. R. (2006). Less is more: Probabilistic models for retrieving fewer relevant documents. En Proceedings of the 29th ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR 2006. ACM, New York, NY, pp. 429-436.
  • Chen, H. & Lynch, K. J. (1992). Automatic construction of networks of concepts characterizing document databases. IEEE Transactions on Systems, Man, and Cybernetics 22(5), pp. 885-902.
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  • Cohen, W., Hurst, M. & Jensen, L. (2002). A flexible learning system for wrapping tables and lists in HTML documents. Proceedings of the 11th International World Wide Web Conference, pp. 232-241.
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D

  • Dang, V., Bendersky, M. & Croft, W. B. (2010). Learning to Rank query reformulations. En Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR 2010.
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E

  • Edmundson, H. P. (1969). New methods in automatic extracting. Journal of the Association for Computing Machinery, 16(2), pp. 264-285.
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F

  • Fernández, R.T. (2007). The Effect Of Smoothing In Language Models For Novelty Detection. En Proceedings of BCS-IRSG Symposium: Future Directions in Information Access. FDIA’07, pp. 11-16.
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G

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H

  • Harman, D. K. (1992). Relevance feedback and other query modification techniques. En Frakes, W.B. & Baeza-Yates, R. (Eds.). Information Retrieval: Data Structures and Algorithms. Prentice-Hall, pp. 241-263.
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  • Harman, D. K. (2005). Beyond English. En Voorhees, E. M. & Harman D. K. (Eds). TREC: experiment and evaluation in information retrieval. The MIT Press.
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  • He, B., Macdonald, C., He, J. & Ounis, I. (2008). An effective statistical approach to blog post opinion retrieval. En Proceeding of the 17th ACM Conference on Information and Knowledge Management. CIKM 2008. ACM, pp. 1063-1072.
  • He, B., Macdonald, C. & Ounis, I. (2008a). Ranking opinionated blog posts using OpinionFinder. En Proceedings of the 31st ACM SIGIR Conference on Research and Development in Information Retrieval SIGIR 2008. ACM, 2008, pp. 727-728.
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  • Hedin, B., Tomlinson, S., Baron, J. R. & Oard, D.W. (2009). Overview of the TREC 2009 Legal Track. Proceedings of the Text REtrieval Conference. TREC 2009.
  • Heinz, S & Zobel, J. (2003). Efficient Single-Pass index construction for text databases, Journal of the American Society for Information Science and Technology, JASIST, 54(8), pp. 713-729.
  • Hendler, J. (2001). Agents and the Semantic Web. IEEE Intelligent Systems 16(2), pp. 30-37.
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  • Huvila, I. (2010). Where does the information come from? Information source use patterns in Wikipedia. Information Research, 15(3). En http://informationr.net/ir/15-3/paper433.html

I

  • Ingwersen, P. & Järvelin, K. (2010). The Turn. Integration of Information Seeking and Retrieval in Context. Dordrecht: Springer.

J

  • Jain, A. K., Murty, M. N. & Flynn, P. J. (1999). Data clustering: a review. ACM Computing Surveys, 31, 3, pp. 264-323.
  • Jansen, B. J. & Spink, A (2006). How are we searching the World Wide Web? A comparison of nine search engine transaction logs. Information Processing & Management, 42(1), pp. 248-263.
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  • Järvelin, K. & Kekäläinen, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems, 20(4), pp. 422-446.
  • Java, A., Kolari, P., Finin, T., Joshi, C. & Oates, T. (2007). Feeds That Matter: A Study of Bloglines Subscriptions. En Proceedings of the International Conference on Weblogs and Social Media (ICWSM 2007). Computer Science and Electrical Engineering.
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