Relevant bibliographies by topics / Information Retrieval Model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Information Retrieval Model'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "Information Retrieval Model"

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Qiao, Hu, Qingyun Wu, Songlin Yu, Jiang Du, and Ying Xiang. "A 3D assembly model retrieval method based on assembly information." Assembly Automation 39, no. 4 (2019): 556–65. http://dx.doi.org/10.1108/aa-03-2018-047.

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Purpose The purpose of this paper is to propose a three-dimensional (3D) assembly model retrieval method based on assembling semantic information to address semantic mismatches, poor accuracy and low efficiency in existing 3D assembly model retrieval methods. Design/methodology/approach The paper proposes an assembly model retrieval method. First, assembly information retrieval is performed, and 3D models that conform to the design intention of the assembly are found by retrieving the code. On this basis, because there are conjugate subgraphs between attributed adjacency graphs (AAG) that have an assembly relationship, the assembly model geometric retrieval is translated into a problem of finding AAGs with a conjugate subgraph. Finally, the frequent subgraph mining method is used to retrieve AAGs with conjugate subgraphs. Findings The method improved the efficiency and accuracy of assembly model retrieval. Practical implications The examples illustrate the specific retrieval process and verify the feasibility and reasonability of the assembly model retrieval method in practical applications. Originality/value The assembly model retrieval method in the paper is an original method. Compared with other methods, good results were obtained.
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Zhou, Minqiang, Bavo Langerock, Mahesh Kumar Sha, et al. "Retrieval of atmospheric CH<sub>4</sub> vertical information from ground-based FTS near-infrared spectra." Atmospheric Measurement Techniques 12, no. 11 (2019): 6125–41. http://dx.doi.org/10.5194/amt-12-6125-2019.

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Abstract. The Total Carbon Column Observing Network (TCCON) column-averaged dry air mole fraction of CH4 (XCH4) measurements have been widely used to validate satellite observations and to estimate model simulations. The GGG2014 code is the standard TCCON retrieval software used in performing a profile scaling retrieval. In order to obtain several vertical pieces of information in addition to the total column, in this study, the SFIT4 retrieval code is applied to retrieve the CH4 mole fraction vertical profile from the Fourier transform spectrometer (FTS) spectrum at six sites (Ny-Ålesund, Sodankylä, Bialystok, Bremen, Orléans and St Denis) during the time period of 2016–2017. The retrieval strategy of the CH4 profile retrieval from ground-based FTS near-infrared (NIR) spectra using the SFIT4 code (SFIT4NIR) is investigated. The degree of freedom for signal (DOFS) of the SFIT4NIR retrieval is about 2.4, with two distinct pieces of information in the troposphere and in the stratosphere. The averaging kernel and error budget of the SFIT4NIR retrieval are presented. The data accuracy and precision of the SFIT4NIR retrievals, including the total column and two partial columns (in the troposphere and stratosphere), are estimated by TCCON standard retrievals, ground-based in situ measurements, Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS) satellite observations, TCCON proxy data and AirCore and aircraft measurements. By comparison against TCCON standard retrievals, it is found that the retrieval uncertainty of SFIT4NIR XCH4 is similar to that of TCCON standard retrievals with systematic uncertainty within 0.35 % and random uncertainty of about 0.5 %. The tropospheric and stratospheric XCH4 from SFIT4NIR retrievals are assessed by comparison with AirCore and aircraft measurements, and there is a 1.0 ± 0.3 % overestimation in the SFIT4NIR tropospheric XCH4 and a 4.0 ± 2.0 % underestimation in the SFIT4NIR stratospheric XCH4, which are within the systematic uncertainties of SFIT4NIR-retrieved partial columns in the troposphere and stratosphere respectively.
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Negi, Pritam Singh, M. M. S. Rauthan, and H. S. Dhami. "Language Model for Information Retrieval." International Journal of Computer Applications 12, no. 7 (2010): 13–17. http://dx.doi.org/10.5120/1692-2197.

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BORDOGNA, GLORIA, and GABRIELLA PASI. "AN ORDINAL INFORMATION RETRIEVAL MODEL." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 09, supp01 (2001): 63–75. http://dx.doi.org/10.1142/s0218488501000995.

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In this paper an ordinal Information Retrieval model is proposed, which is formalised within fuzzy set theory and is based on the notion of linguistic granules of information. Linguistic expressions are defined to represent and manage the importance of both the index terms as descriptors of the information items and the query terms (content selectors) as descriptors of users' needs. The advantage of this approach with respect to the (numeric) fuzzy IR models is that the query evaluation mechanism and the definition of the importance semantics are simplified.
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Zadrożny, Sławomir, and Katarzyna Nowacka. "Fuzzy information retrieval model revisited." Fuzzy Sets and Systems 160, no. 15 (2009): 2173–91. http://dx.doi.org/10.1016/j.fss.2009.02.012.

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Wood, Norman B., and Tristan S. L'Ecuyer. "What millimeter-wavelength radar reflectivity reveals about snowfall: an information-centric analysis." Atmospheric Measurement Techniques 14, no. 2 (2021): 869–88. http://dx.doi.org/10.5194/amt-14-869-2021.

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Abstract. The ability of single-frequency, millimeter-wavelength radar reflectivity observations to provide useful constraints for retrieval of snow particle size distribution (PSD) parameters, snowfall rates, and snowfall accumulations is examined. An optimal estimation snowfall retrieval that allows analyses of retrieval uncertainties and information content is applied to observations of near-surface W-band reflectivities from multiple snowfall events during the 2006–2007 winter season in southern Ontario. Retrieved instantaneous snowfall rates generally have uncertainties greater than 100 %, but single-event and seasonal snow accumulations from the retrieval results match well with collocated measurements of accumulations. Absolute fractional differences are mainly below 30 % for individual events that have more substantial accumulations and, for the season, 12.6 %. Uncertainties in retrieved snowfall rates are driven mainly by uncertainties in the retrieved PSD parameters, followed by uncertainties in particle model parameters and, to a lesser extent, the uncertainties in the fall-speed model. Uncertainties attributable to assuming an exponential distribution are negligible. The results indicate that improvements to PSD and particle model a priori constraints provide the most impactful path forward for reducing uncertainties in retrieved snowfall rates. Information content analyses reveal that PSD slope is well-constrained by the retrieval. Given the sensitivity of PSD slope to microphysical transformations, the results show that such retrievals, when applied to radar reflectivity profiles, could provide information about microphysical transformations in the snowing column. The PSD intercept is less well-constrained by the retrieval. While applied to near-surface radar observations in this study, the retrieval is applicable as well to radar observations aloft, such as those provided by profiling ground-based, airborne, and satellite-borne radars under lighter snowfall conditions when attenuation and multiple scattering can be neglected.
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Sheng, Zhong Biao, Hua Ping Jia, and Xiao Rong Tong. "Design of Personalized Intelligent Information Retrieval Model Based on Agent." Applied Mechanics and Materials 155-156 (February 2012): 1175–79. http://dx.doi.org/10.4028/www.scientific.net/amm.155-156.1175.

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The features of vast distributed dynamic information on Web caused the problem of “overload” and “mislead” while query. Intelligent agent is a way to solve it. After considering the problems of users’ personal interests during the information retrieve adequately, the paper proposes an intelligent information retrieval model based-on Agent. This system integrated domain knowledge and used many arithmetic of learning user’s interest. Each Agent co-operates to finish information retrieval task, manifest the characteristics of intellectualization and individuality of in information retrieval. It is a good way to realize the highly effective intelligent retrieval system research.
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Dr. V. Suma. "A Novel Information retrieval system for distributed cloud using Hybrid Deep Fuzzy Hashing Algorithm." September 2020 02, no. 03 (2020): 151–60. http://dx.doi.org/10.36548/jitdw.2020.3.003.

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The recent technology development fascinates the people towards information and its services. Managing the personal and pubic data is a perennial research topic among researchers. In particular retrieval of information gains more attention as it is important similar to data storing. Clustering based, similarity based, graph based information retrieval systems are evolved to reduce the issues in conventional information retrieval systems. Learning based information retrieval is the present trend and in particular deep neural network is widely adopted due to its retrieval performance. However, the similarity between the information has uncertainties due to its measuring procedures. Considering these issues also to improve the retrieval performance, a hybrid deep fuzzy hashing algorithm is introduced in this research work. Hashing efficiently retrieves the information based on mapping the similar information as correlated binary codes and this underlying information is trained using deep neural network and fuzzy logic to retrieve the necessary information from distributed cloud. Experimental results prove that the proposed model attains better retrieval accuracy and accuracy compared to conventional models such as support vector machine and deep neural network.
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Yu, Yang Xin. "Personalization Information Retrieval Based on Unigram Language Model." Applied Mechanics and Materials 321-324 (June 2013): 2269–73. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.2269.

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Personalization information retrieval is very useful in information retrieval system, the user profile can be used to represent the favorites or interests of user. Many methods to personalization have been studied in extending query with user profile. A proposed navel method which use the context of long-term user profile with multiple domain to extend query model under the unigram language model framework, uses the new query model to retrieve and get more interesting results for users. Combined with psudo relevance feedback model, the proposed method get better performance. Experimental results show that the proposed method in this paper is effective.
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Zhu, Qiuyu, Dongmei Li, Cong Dai, Qichen Han, and Yi Lin. "PLSA-Based Personalized Information Retrieval with Network Regularization." Journal of Information Technology Research 12, no. 1 (2019): 105–16. http://dx.doi.org/10.4018/jitr.2019010108.

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With the rapid development of the Internet, the information retrieval model based on the keywords matching algorithm has not met the requirements of users, because people with various query history always have different retrieval intentions. User query history often implies their interests. Therefore, it is of great importance to enhance the recall ratio and the precision ratio by applying query history into the judgment of retrieval intentions. For this sake, this article does research on user query history and proposes a method to construct user interest model utilizing query history. Coordinately, the authors design a model called PLSA-based Personalized Information Retrieval with Network Regularization. Finally, the model is applied into academic information retrieval and the authors compare it with Baidu Scholar and the personalized information retrieval model based on the probabilistic latent semantic analysis topic model. The experiment results prove that this model can effectively extract topics and retrieves back results more satisfied for users' requirements. Also, this model improves the effect of retrieval results apparently. In addition, the retrieval model can be utilized not only in the academic information retrieval, but also in the personalized information retrieval on microblog search, associate recommendation, etc.
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Dissertations / Theses on the topic "Information Retrieval Model"

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Paulsen, Jon Rune. "Optimal Information Retrieval Model for Molecular Biology Information." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8718.

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<p>Search engines for biological information are not a new technology. Since the 1960s computers have emerged as an important tool for biologists. Online Mendelian Inheritance in Man (OMIM) is a comprehensive catalogue containing approximately 14 000 records with information about human genes and genetic disorders. An approach called Latent Semantic Indexing (LSI) was introduced in 1990 that is based on Singular Value Decomposition (SVD). This approach improved the information retrieval and reduced the storage requirements. This thesis applies LSI on the collection of OMIM records. To further improve the retrieval effectiveness and efficiency, the author propose a clustering method based on the standard k-means algorithm, called Two step k-means. Both the standard k-means and the Two step k-means algorithms are tested and compared with each other.</p>
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Tanase, Diana. "A model for information retrieval driven by conceptual spaces." Thesis, University of Westminster, 2015. https://westminsterresearch.westminster.ac.uk/item/972v9/a-model-for-information-retrieval-driven-by-conceptual-spaces.

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A retrieval model describes the transformation of a query into a set of documents. The question is: what drives this transformation? For semantic information retrieval type of models this transformation is driven by the content and structure of the semantic models. In this case, Knowledge Organization Systems (KOSs) are the semantic models that encode the meaning employed for monolingual and cross-language retrieval. The focus of this research is the relationship between these meanings’ representations and their role and potential in augmenting existing retrieval models effectiveness. The proposed approach is unique in explicitly interpreting a semantic reference as a pointer to a concept in the semantic model that activates all its linked neighboring concepts. It is in fact the formalization of the information retrieval model and the integration of knowledge resources from the Linguistic Linked Open Data cloud that is distinctive from other approaches. The preprocessing of the semantic model using Formal Concept Analysis enables the extraction of conceptual spaces (formal contexts)that are based on sub-graphs from the original structure of the semantic model. The types of conceptual spaces built in this case are limited by the KOSs structural relations relevant to retrieval: exact match, broader, narrower, and related. They capture the definitional and relational aspects of the concepts in the semantic model. Also, each formal context is assigned an operational role in the flow of processes of the retrieval system enabling a clear path towards the implementations of monolingual and cross-lingual systems. By following this model’s theoretical description in constructing a retrieval system, evaluation results have shown statistically significant results in both monolingual and bilingual settings when no methods for query expansion were used. The test suite was run on the Cross-Language Evaluation Forum Domain Specific 2004-2006 collection with additional extensions to match the specifics of this model.
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Gómez, Luis Alfonso. "A two-level model for information retrieval systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/mq24469.pdf.

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Adriani, Mirna. "A query ambiguity model for cross-language information retrieval." Thesis, University of Glasgow, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.407678.

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Tan, Kok Fong. "Extending information retrieval system model to improve interactive web searching." Thesis, Middlesex University, 2005. http://eprints.mdx.ac.uk/8027/.

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The research set out with the broad objective of developing new tools to support Web information searching. A survey showed that a substantial number of interactive search tools were being developed but little work on how these new developments fitted into the general aim of helping people find information. Due to this it proved difficult to compare and analyse how tools help and affect users and where they belong in a general scheme of information search tools. A key reason for a lack of better information searching tools was identified in the ill-suited nature of existing information retrieval system models. The traditional information retrieval model is extended by synthesising work in information retrieval and information seeking research. The purpose of this new holistic search model is to assist information system practitioners in identifying, hypothesising, designing and evaluating Web information searching tools. Using the model, a term relevance feedback tool called 'Tag and Keyword' (TKy) was developed in a Web browser and it was hypothesised that it could improve query reformulation and reduce unnecessary browsing. The tool was laboratory experimented and quantitative analysis showed statistical significances in increased query reformulations and in reduced Web browsing (per query). Subjects were interviewed after the experiment and qualitative analysis revealed that they found the tool useful and saved time. Interestingly, exploratory analysis on collected data identified three different methods in which subjects had utilised the TKy tool. The research developed a holistic search model for Web searching and demonstrated that it can be used to hypothesise, design and evaluate information searching tools. Information system practitioners using it can better understand the context in which their search tools are developed and how these relate to users' search processes and other search tools.
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Muoh, Chibuike. "Sparsification for Topic Modeling and Applications to Information Retrieval." Kent State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=kent1259206719.

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Yeung, Chung Kei. "Ontological model for information systems development methodology." HKBU Institutional Repository, 2006. http://repository.hkbu.edu.hk/etd_ra/702.

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Krishnan, Sharenya. "Text-Based Information Retrieval Using Relevance Feedback." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-53603.

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Europeana, a freely accessible digital library with an idea to make Europe's cultural and scientific heritage available to the public was founded by the European Commission in 2008. The goal was to deliver a semantically enriched digital content with multilingual access to it. Even though they managed to increase the content of data they slowly faced the problem of retrieving information in an unstructured form. So to complement the Europeana portal services, ASSETS (Advanced Search Service and Enhanced Technological Solutions) was introduced with services that sought to improve the usability and accessibility of Europeana. My contribution is to study different text-based information retrieval models, their relevance feedback techniques and to implement one simple model. The thesis explains a detailed overview of the information retrieval process along with the implementation of the chosen strategy for relevance feedback that generates automatic query expansion. Finally, the thesis concludes with the analysis made using relevance feedback, discussion on the model implemented and then an assessment on future use of this model both as a continuation of my work and using this model in ASSETS.
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Huang, Huan. "Development of a conceptual graph based information retrieval model for medical question databases /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1421143.

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Lin, Xing. "Modern GIR Systems : Framework, Retrieval Model and Indexing Techniques." Licentiate thesis, KTH, Geoinformatik och Geodesi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-34787.

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Geographic information is one of the most important and the most common types of information in human society. It is estimated that more than 70% of all information in the world has some kind of geographic features. In the era of information explosion, information retrieval (IR) tools, such as search engines, are the main tools people used to quickly find the information they need nowadays. Because of the importance of geographic information, recent efforts have been made either by expanding the traditional IR to support a spatial query, or building a GIR in a brand new architecture from the ground such as the SPIRIT project. To some degree, these existing GIR systems could solve users’ information search need with a spatial filter, especially when the users are looking for information on something within a relatively large extent.Despite its advantage on processing geographical information and queries over conventional IR systems, modern GIR systems are also facing challenges including a proper representation and extraction of geographical information within documents, a better information retrieval model for both thematic and geographical information, a fast indexing mechanism for rapid search within documents by thematic and geographical hints, and even a new architecture of system.The objective of this licentiate research is to provide solutions to some of these problems in order to build a better modern GIR system in the future. The following aspects have been investigated in the thesis: a generic conceptual framework and related key technologies for a modern GIR system, a new information retrieval model and algorithm for measuring the relevance scores between documents and queries in GIR, and finally a new better indexing technique to geographically and thematically index the documents for a faster query processing within modern GIR.Concerning the proposed conceptual framework for modern GIR, it includes three modules: (1) the user interface module, (2) the information extractor, storage and indexer module and (3) the query processing and information retrieval module. Two knowledge bases, Gazetteer and Thesaurus, play an important role in the proposed framework. A digital map based user interface is proposed for the input of user information search needs and representation of retrieval results. Key techniques required for the implementation of a modern GIR using the proposed framework are a proper representation of document and query information, a better geographical information extractor, an innovative information retrieval model and relevance ranking algorithm, and a combined indexing mechanism for both geographical and thematic information.The new information retrieval model is established based on a Spatial Bayesian Network consisting of place names appeared in a single document and the spatial relationships between them. The new model assesses the geographical relevance between GIR document and query by the geographical importance and adjacency of the document geo-footprint versus the geographical scope of the user’s query.Regarding the indexing mechanism for modern GIR systems, a Keyword-Spatial Hybrid Index (KSHI) is proposed for the single and overall geo-footprint model, in which there is only one single geo-footprint for each document to retrieve from. A Keyword-Spatial Dual Index (KSDI) is proved to be more appropriate for a GIR system which allows for multiple geo-footprints within a single document.In addition to theoretical analysis, necessary experiments have also been carried out to evaluate the efficiency of proposed new information retrieval model and indices. Both the theoretical analysis and results of experiments show the potentials of proposed solution and techniques.<br>QC 20110630
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Books on the topic "Information Retrieval Model"

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Australian Institute of Health and Welfare. National health information model: Version 2. Australian Institute of Health and Welfare, 2003.

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Hert, Carol A. Exploring a new model for the understanding of information retrieval interactions. University Microfilms International, 1995.

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Ellis, David B. The derivation of a behavioural model for information retrieval system design. University Microfilms International, 1987.

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Doedens, Crist-Jan. Text databases: One database model and several retrieval languages. Rodopi, 1994.

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Mete, Celik, and SpringerLink (Online service), eds. Spatial AutoRegression (SAR) Model: Parameter Estimation Techniques. Springer US, 2012.

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Yu, Faxin. Three-dimensional model analysis and processing. Zhejiang University Press, 2010.

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Fausto, Giunchiglia, Tanca Letizia, and SpringerLink (Online service), eds. Semantic Web Information Management: A Model-Based Perspective. Springer-Verlag Berlin Heidelberg, 2010.

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MacDougall, Edward Bruce. The feasibility of geographic information systems: A model study. Massachusetts Agricultural Experiment Station, College of Food and Natural Resources, 1987.

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Monprode-Holt, Lorrie. The Montana library and information skills model curriculum guide. Office of Public Instruction, 1994.

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Montana. Office of Public Instruction. The Montana library and information skills model curriculum guide. The Office, 1993.

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Book chapters on the topic "Information Retrieval Model"

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Na, Seung-Hoon, In-Su Kang, Sin-Jae Kang, and Jong-Hyeok Lee. "Estimation of Query Model from Parsimonious Translation Model." In Information Retrieval Technology. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31871-2_21.

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Ganguly, Debasis, Johannes Leveling, and Gareth J. F. Jones. "Topical Relevance Model." In Information Retrieval Technology. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35341-3_28.

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Meghini, Carlo, Fabrizio Sebastiani, and Umberto Straccia. "Multimedia Information Retrieval Model." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_233.

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Meghini, Carlo, Fabrizio Sebastiani, and Umberto Straccia. "Multimedia Information Retrieval Model." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_233.

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Meghini, Carlo, Fabrizio Sebastiani, and Umberto Straccia. "Multimedia Information Retrieval Model." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_233-2.

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Na, Seung-Hoon, In-Su Kang, Ji-Eun Roh, and Jong-Hyeok Lee. "Effective Query Model Estimation Using Parsimonious Translation Model in Language Modeling Approach." In Information Retrieval Technology. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11562382_22.

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Hiong, Siaw Nyuk, Bali Ranaivo-Malançon, Narayanan Kulathuramaiyer, and Jane Labadin. "Linguistically Enhanced Collocate Words Model." In Information Retrieval Technology. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12844-3_20.

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Aliannejadi, Mohammad, Ida Mele, and Fabio Crestani. "User Model Enrichment for Venue Recommendation." In Information Retrieval Technology. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48051-0_16.

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Luo, Cheng, Yiqun Liu, Min Zhang, and Shaoping Ma. "Search Success Evaluation with Translation Model." In Information Retrieval Technology. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48051-0_19.

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Lu, Sha, Ben He, and Jungang Xu. "Hyper-geometric Model for Information Retrieval Revisited." In Information Retrieval Technology. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45068-6_6.

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Conference papers on the topic "Information Retrieval Model"

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Al-Dubaee, Shawki A. "New information retrieval model." In 2014 Science and Information Conference (SAI). IEEE, 2014. http://dx.doi.org/10.1109/sai.2014.6918280.

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Zuo, Jiali, Mingwen Wang, Jianyi Wan, and Wenbing Luo. "Information Retrieval Model Combining Sentence Level Retrieval." In 2013 International Conference on Asian Language Processing (IALP). IEEE, 2013. http://dx.doi.org/10.1109/ialp.2013.76.

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Meng-Fei, Zhang, and Sui Ai-Na. "Personalized Multimedia-Information Retrieval Model." In 2012 International Conference on Industrial Control and Electronics Engineering (ICICEE). IEEE, 2012. http://dx.doi.org/10.1109/icicee.2012.121.

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Anand, Abhishek, Upasana Sharma, and Devender Kumar. "Information Retrieval in Computing Model." In 2019 International Conference on Intelligent Computing and Control Systems (ICCS). IEEE, 2019. http://dx.doi.org/10.1109/iccs45141.2019.9065562.

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Caetano, Artur, and Nuno Guimarães. "A Model for Content Representation of Multimedia Information." In Challenge of Image Retrieval. BCS Learning & Development, 1998. http://dx.doi.org/10.14236/ewic/cir1998.1.

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Gao, Jianfeng, Jian-Yun Nie, Guangyuan Wu, and Guihong Cao. "Dependence language model for information retrieval." In the 27th annual international conference. ACM Press, 2004. http://dx.doi.org/10.1145/1008992.1009024.

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Garrouch, Kamel, and Mohamed Nazih Omri. "Bayesian Network Based Information Retrieval Model." In 2017 International Conference on High Performance Computing & Simulation (HPCS). IEEE, 2017. http://dx.doi.org/10.1109/hpcs.2017.38.

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Garrouch, Kamel, and Mohamed Nazih Omri. "Possibilistic Network based Information Retrieval Model." In 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2015. http://dx.doi.org/10.1109/isda.2015.7489255.

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Jin, Rong, Alex G. Hauptmann, and Cheng Xiang Zhai. "Title language model for information retrieval." In the 25th annual international ACM SIGIR conference. ACM Press, 2002. http://dx.doi.org/10.1145/564376.564386.

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Gao, Huiying, Jinghua Zhao, Qiuju Yin, and Jingxia Wang. "Ontology-based enterprise information retrieval model." In 2009 IEEE International Conference on Grey Systems and Intelligent Services (GSIS 2009). IEEE, 2009. http://dx.doi.org/10.1109/gsis.2009.5408118.

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Reports on the topic "Information Retrieval Model"

1

Ng, Kenney. A Maximum Likelihood Ratio Information Retrieval Model. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada456243.

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Price, Susan. Semantic Components: A Model for Enhancing Retrieval of Domain- Specific Information. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.2670.

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Elkerton, Jay, and Susan Palmiter. Designing Help Systems Using a GOMS Model: Part 1. An Information Retrieval Evaluation. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada213603.

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4

Paskaleva, Biliana Stefanova, Pavel B. Bochev, and Arlo Leroy Ames. An extended vector space model for information retrieval with generalized similarity measures : theory and applications. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1055624.

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