Academic literature on the topic 'Information Retrieva'
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Journal articles on the topic "Information Retrieva"
Frankenberg, C., O. Hasekamp, C. O'Dell, S. Sanghavi, A. Butz, and J. Worden. "Aerosol information content analysis of multi-angle high spectral resolution measurements and its benefit for high accuracy greenhouse gas retrievals." Atmospheric Measurement Techniques Discussions 5, no. 2 (April 16, 2012): 2857–85. http://dx.doi.org/10.5194/amtd-5-2857-2012.
Full textFrankenberg, C., O. Hasekamp, C. O'Dell, S. Sanghavi, A. Butz, and J. Worden. "Aerosol information content analysis of multi-angle high spectral resolution measurements and its benefit for high accuracy greenhouse gas retrievals." Atmospheric Measurement Techniques 5, no. 7 (July 27, 2012): 1809–21. http://dx.doi.org/10.5194/amt-5-1809-2012.
Full textZhou, Minqiang, Bavo Langerock, Mahesh Kumar Sha, Nicolas Kumps, Christian Hermans, Christof Petri, Thorsten Warneke, et al. "Retrieval of atmospheric CH<sub>4</sub> vertical information from ground-based FTS near-infrared spectra." Atmospheric Measurement Techniques 12, no. 11 (November 25, 2019): 6125–41. http://dx.doi.org/10.5194/amt-12-6125-2019.
Full textJalali, Ali, Shannon Hicks-Jalali, Robert J. Sica, Alexander Haefele, and Thomas von Clarmann. "A practical information-centered technique to remove a priori information from lidar optimal-estimation-method retrievals." Atmospheric Measurement Techniques 12, no. 7 (July 18, 2019): 3943–61. http://dx.doi.org/10.5194/amt-12-3943-2019.
Full textFournier, N., P. Stammes, M. de Graaf, R. van der A, A. Piters, M. Grzegorski, and A. Kokhanovsky. "Improving cloud information over deserts from SCIAMACHY Oxygen A-band measurements." Atmospheric Chemistry and Physics 6, no. 1 (January 25, 2006): 163–72. http://dx.doi.org/10.5194/acp-6-163-2006.
Full textShi, Chong, Makiko Hashimoto, and Teruyuki Nakajima. "Remote sensing of aerosol properties from multi-wavelength and multi-pixel information over the ocean." Atmospheric Chemistry and Physics 19, no. 4 (February 26, 2019): 2461–75. http://dx.doi.org/10.5194/acp-19-2461-2019.
Full textCha, Ting-Yu, and Michael M. Bell. "Comparison of single-Doppler and multiple-Doppler wind retrievals in Hurricane Matthew (2016)." Atmospheric Measurement Techniques 14, no. 5 (May 18, 2021): 3523–39. http://dx.doi.org/10.5194/amt-14-3523-2021.
Full textBen Ayed, Alaidine, Ismaïl Biskri, and Jean-Guy Meunier. "An End-to-End Efficient Lucene-Based Framework of Document/Information Retrieval." International Journal of Information Retrieval Research 12, no. 1 (January 2022): 1–14. http://dx.doi.org/10.4018/ijirr.289950.
Full textLokhande, Kalyani, and Dhanashree Tayade. "English-Marathi Cross Language Information Retrieval System." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (August 30, 2017): 112. http://dx.doi.org/10.23956/ijarcsse.v7i8.34.
Full textTorabian, Saba, Zhe Chen, Beth A. Ober, and Gregory K. Shenaut. "Analogical Retrieval of Folktales: A Cross-Cultural Approach." Journal of Cognition and Culture 17, no. 3-4 (October 6, 2017): 281–305. http://dx.doi.org/10.1163/15685373-12340008.
Full textDissertations / Theses on the topic "Information Retrieva"
BASSANI, ELIAS. "Neural Approaches to Personalized Search." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2023. https://hdl.handle.net/10281/404515.
Full textThe recent advancements in Neural Networks research have pushed forward the state-of-the-art in many language-related tasks, including Information Retrieval, bringing new opportunities for representing and leveraging user-related information during personalization. However, their application in the context of Personalized Search is still an open research area, with many issues and challenges to be addressed and tackled. In this dissertation, we focus on representing the user preferences from multiple perspectives, managing and selecting the user information to personalize the current search, and improving query representations with user-specific data by proposing new approaches based on Neural Networks. Moreover, we address the lack of publicly available large-scale datasets suited for training and evaluating Neural Networks-based approaches for Personalized Search. We first study the problem of leveraging the user preferences represented from multiple perspectives by proposing a multi-representation re-ranking model. We show that our proposed approach achieves competitive performance while being fast, scalable, and extended to include additional representations and features. We then conduct an in-depth analysis of a Neural Networks mechanism, the Attention, when employed for user modeling, highlighting some shortcomings due to one of its internal components, the Softmax normalization function. We address those shortcomings by introducing a novel Attention variant, the Denoising Attention, that adopts a more robust normalization scheme and employs a filtering mechanism. Experimental evaluations clearly show the benefits of our proposed approach over other Attention variants. Furthermore, we address the enhancement of query representations with user-specific data by proposing a novel Personalized Query Expansion approach designed for contextualized word embeddings, which leverages an offline clustering-based procedure to identify the user-related terms that better represent the user interests. We show it improves in terms of retrieval effectiveness over word embedding-based Query Expansion methods at the state-of-the-art while also achieving sub-millisecond expansion time thanks to an approximation we propose. Finally, we discuss the state of Personalized Information Retrieval evaluation and the available publicly available datasets and propose and share a novel large-scale benchmark across four domains, with more than 18 million documents and 1.9 million queries. We present a detailed description of the benchmark construction procedure, highlighting its characteristics and challenges, and provide baselines for future works. The solutions and findings presented in this dissertation show that Personalized Search is still an open research area. Moreover, the new opportunities brought to the table by the recent advancements in Neural Networks also introduce new challenges that need to be correctly addressed to both take full advantage of their potential and make them valuable for real-world Personalized Search applications.
Bartow, Paul J. "Information retrieval /." Online version of thesis, 1991. http://hdl.handle.net/1850/12169.
Full textLui, Chang. "Synatic Information Retrieval." Thesis, University of Ulster, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.516287.
Full textDunlop, Mark David. "Multimedia information retrieval." Thesis, University of Glasgow, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358626.
Full textKeim, Michelle. "Bayesian information retrieval /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/8937.
Full textBrucato, Matteo. "Temporal Information Retrieval." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/5690/.
Full textBuscaldi, Davide. "Toponym Disambiguation in Information Retrieval." Doctoral thesis, Universitat Politècnica de València, 2010. http://hdl.handle.net/10251/8912.
Full textBuscaldi, D. (2010). Toponym Disambiguation in Information Retrieval [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8912
Palancia
Morgenroth, Karlheinz. "Kontextbasiertes Information-Retrieval : Modell, Konzeption und Realisierung kontextbasierter Information-Retrieval-Systeme /." Berlin : Logos, 2006. http://deposit.ddb.de/cgi-bin/dokserv?id=2786087&prov=M&dok_var=1&dok_ext=htm.
Full textKoenders, Michael. "FROM MUSIC INFORMATION RETRIEVAL (MIR) TO INFORMATION RETRIEVAL FOR MUSIC (IRM)." Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/16914.
Full textOsodo, Jennifer Akinyi. "An extended vector-based information retrieval system to retrieve e-learning content based on learner models." Thesis, University of Sunderland, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.542053.
Full textBooks on the topic "Information Retrieva"
Lin, Hongfei, Min Zhang, and Liang Pang, eds. Information Retrieval. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88189-4.
Full textGrossman, David A., and Ophir Frieder. Information Retrieval. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-1-4020-3005-5.
Full textFuhr, Norbert, ed. Information Retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76981-8.
Full textZhang, Shichao, Tie-Yan Liu, Xianxian Li, Jiafeng Guo, and Chenliang Li, eds. Information Retrieval. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01012-6.
Full textWen, Jirong, Jianyun Nie, Tong Ruan, Yiqun Liu, and Tieyun Qian, eds. Information Retrieval. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68699-8.
Full textGker, Aye, and John Davies, eds. Information Retrieval. Chichester, UK: John Wiley & Sons, Ltd, 2009. http://dx.doi.org/10.1002/9780470033647.
Full textBraslavski, Pavel, Ilya Markov, Panos Pardalos, Yana Volkovich, Dmitry I. Ignatov, Sergei Koltsov, and Olessia Koltsova, eds. Information Retrieval. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41718-9.
Full textDou, Zhicheng, Qiguang Miao, Wei Lu, Jiaxin Mao, and Guang Jia, eds. Information Retrieval. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56725-5.
Full textZhang, Qi, Xiangwen Liao, and Zhaochun Ren, eds. Information Retrieval. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31624-2.
Full textHersh, William. Information Retrieval. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-78703-9.
Full textBook chapters on the topic "Information Retrieva"
van Rijsbergen, C. J. "Information retrieval and informative reasoning." In Computer Science Today, 549–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/bfb0015266.
Full textKlinke, Harald. "Information Retrieval." In Digital Humanities, 268–78. Stuttgart: J.B. Metzler, 2017. http://dx.doi.org/10.1007/978-3-476-05446-3_19.
Full textShahi, Dikshant. "Information Retrieval." In Apache Solr, 39–56. Berkeley, CA: Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-1070-3_3.
Full textShekhar, Shashi, and Hui Xiong. "Information Retrieval." In Encyclopedia of GIS, 577. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_636.
Full textHarvey, Charles, and Jon Press. "Information Retrieval." In Databases in Historical Research, 147–87. London: Macmillan Education UK, 1996. http://dx.doi.org/10.1007/978-1-349-24392-1_6.
Full textRiggert, Wolfgang. "Information Retrieval." In Betriebliche Informationskonzepte, 75–126. Wiesbaden: Vieweg+Teubner Verlag, 2000. http://dx.doi.org/10.1007/978-3-322-89195-2_3.
Full textCarneiro, Davide, Paulo Novais, and José Neves. "Information Retrieval." In Conflict Resolution and its Context, 141–62. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06239-6_7.
Full textAmati, Giambattista. "Information Retrieval." In Encyclopedia of Database Systems, 1–6. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_915-2.
Full textDarwish, Kareem. "Information Retrieval." In Natural Language Processing of Semitic Languages, 299–334. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-45358-8_10.
Full textLinckels, Serge, and Christoph Meinel. "Information Retrieval." In X.media.publishing, 81–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17743-9_5.
Full textConference papers on the topic "Information Retrieva"
Ball, Liezl, and Theo Bothma. "The capability of search tools to retrieve words with specific properties from large text collections." In ISIC: the Information Behaviour Conference. University of Borås, Borås, Sweden, 2020. http://dx.doi.org/10.47989/irisic2030.
Full textMorris, Elissa, and Daniel A. McAdams. "Bioinspired Origami: Case Studies Using a Keyword Search Algorithm." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22228.
Full textYim, Sungshik, and David Rosen. "Case-Based Retrieval Approach of Supporting Process Planning in Layer-Based Additive Manufacturing." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35309.
Full textWood, Stephen L. "Function Driven Design Selection of Plastic Injection Molding Features." In ASME 1996 Design Engineering Technical Conferences and Computers in Engineering Conference. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/96-detc/dfm-1301.
Full textLiu, Xu, and F. J. Murcray. "N2O Vertical Profiles Retrieved from Ground-based Solar Absorption Spectra Taken at McMurdo Station During Austral Spring of 1989." In Optical Remote Sensing of the Atmosphere. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/orsa.1995.tuc5.
Full textLi, Zhanjun, Victor Raskin, and Karthik Ramani. "Developing Ontologies for Engineering Information Retrieval." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34530.
Full textLiang, Yan, Wen Feng Lu, Ying Liu, and Soon Chong Johnson Lim. "Interactive Interface Design for Design Rationale Search and Retrieval." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28392.
Full textVitsentiy, Vitaliy. "A stochastic programming approach to optimization of information retrieval." In International Workshop of "Stochastic Programming for Implementation and Advanced Applications". The Association of Lithuanian Serials, 2012. http://dx.doi.org/10.5200/stoprog.2012.22.
Full textLi, Min, J. Y. H. Fuh, Y. F. Zhang, and Z. M. Qiu. "General and Partial Shape Matching Approaches on Feature-Based CAD Models to Support Efficient Part Retrieval." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49159.
Full textMorris, Elissa, and Daniel A. McAdams. "Development of a Keyword Search Algorithm for Bioinspired Design of Foldable Engineering Applications." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67958.
Full textReports on the topic "Information Retrieva"
Jha, Somesh, Vitaly Shmatikov, and Matthew Fredrikson. Private Information Retrieval. Fort Belvoir, VA: Defense Technical Information Center, December 2010. http://dx.doi.org/10.21236/ada536856.
Full textKnoblock, Craig A., Yigal Arens, and Chu-Nan Hsu. Cooperating Agents for Information Retrieval. Fort Belvoir, VA: Defense Technical Information Center, May 1994. http://dx.doi.org/10.21236/ada285887.
Full textHoeferlin, David M., and Stephen A. Thorn. Crosslingual Audio Information Retrieval Development. Fort Belvoir, VA: Defense Technical Information Center, April 2009. http://dx.doi.org/10.21236/ada539725.
Full textNewitt, L. R., G. V. Haines, and R. L. Coles. The magnetic information retrieval program. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/225655.
Full textBader, Brett William, Peter Chew, Ahmed Abdelali, and Tamara Gibson Kolda. Cross-language information retrieval using PARAFAC2. Office of Scientific and Technical Information (OSTI), May 2007. http://dx.doi.org/10.2172/908061.
Full textLiu, Xiaoyong, and W. B. Croft. Statistical Language Modeling for Information Retrieval. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada440321.
Full textFranz, Martin, J. S. McCarley, and Wei-Jing Zhu. English-Chinese Information Retrieval at IBM. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada456312.
Full textLynch, C. Using the Z39.50 Information Retrieval Protocol. RFC Editor, December 1994. http://dx.doi.org/10.17487/rfc1729.
Full textNewitt, L. R., and G. V. Haines. An interactive magnetic information retrieval program. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1985. http://dx.doi.org/10.4095/315221.
Full textNewitt, L. R., and G. V. Haines. An interactive magnetic information retrieval program. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1985. http://dx.doi.org/10.4095/225672.
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