Relevant bibliographies by topics / Matching

Contents

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

Academic literature on the topic 'Matching'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 2025

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Journal articles on the topic "Matching"

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CHENG, EDDIE, and SACHIN PADMANABHAN. "MATCHING PRECLUSION AND CONDITIONAL MATCHING PRECLUSION FOR CROSSED CUBES." Parallel Processing Letters 22, no. 02 (May 16, 2012): 1250005. http://dx.doi.org/10.1142/s0129626412500053.

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The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost-perfect matchings. For many interconnection networks, the optimal sets are precisely those induced by a single vertex. Recently, the conditional matching preclusion number of a graph was introduced to look for obstruction sets beyond those induced by a single vertex. It is defined to be the minimum number of edges whose deletion results in a graph with no isolated vertices that has neither perfect matchings nor almost-perfect matchings. In this paper, we find the matching preclusion number and the conditional matching preclusion number with the classification of the optimal sets for the class of crossed cubes, an important variant of the class of hypercubes. Indeed, we will establish more general results on the matching preclusion and the conditional matching preclusion problems for a larger class of interconnection networks.
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MAO, YAPING, and EDDIE CHENG. "A Concise Survey of Matching Preclusion in Interconnection Networks." Journal of Interconnection Networks 19, no. 03 (September 2019): 1940006. http://dx.doi.org/10.1142/s0219265919400061.

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The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost-perfect matchings. There are other related parameters and generalization including the strong matching preclusion number, the conditional matching preclusion number, the fractional matching preclusion number, and so on. In this survey, we give an introduction on the general topic of matching preclusion.
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Chen, Ciping. "Matchings and matching extensions in graphs." Discrete Mathematics 186, no. 1-3 (May 1998): 95–103. http://dx.doi.org/10.1016/s0012-365x(97)00182-9.

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LÜ, HUAZHONG, and TINGZENG WU. "Fractional Matching Preclusion for Restricted Hypercube-Like Graphs." Journal of Interconnection Networks 19, no. 03 (September 2019): 1940010. http://dx.doi.org/10.1142/s0219265919400103.

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The restricted hypercube-like graphs, variants of the hypercube, were proposed as desired interconnection networks of parallel systems. The matching preclusion number of a graph is the minimum number of edges whose deletion results in the graph with neither perfect matchings nor almost perfect matchings. The fractional perfect matching preclusion and fractional strong perfect matching preclusion are generalizations of the matching preclusion. In this paper, we obtain fractional matching preclusion number and fractional strong matching preclusion number of restricted hypercube-like graphs, which extend some known results.
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Echenique, Federico, SangMok Lee, Matthew Shum, and M. Bumin Yenmez. "Stability and Median Rationalizability for Aggregate Matchings." Games 12, no. 2 (April 9, 2021): 33. http://dx.doi.org/10.3390/g12020033.

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We develop the theory of stability for aggregate matchings used in empirical studies and establish fundamental properties of stable matchings including the result that the set of stable matchings is a non-empty, complete, and distributive lattice. Aggregate matchings are relevant as matching data in revealed preference theory. We present a result on rationalizing a matching data as the median stable matching.
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Cannas, Massimo, and Emiliano Sironi. "Optimal Matching with Matching Priority." Analytics 3, no. 1 (March 19, 2024): 165–77. http://dx.doi.org/10.3390/analytics3010009.

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Matching algorithms are commonly used to build comparable subsets (matchings) in observational studies. When a complete matching is not possible, some units must necessarily be excluded from the final matching. This may bias the final estimates comparing the two populations, and thus it is important to reduce the number of drops to avoid unsatisfactory results. Greedy matching algorithms may not reach the maximum matching size, thus dropping more units than necessary. Optimal matching algorithms do ensure a maximum matching size, but they implicitly assume that all units have the same matching priority. In this paper, we propose a matching strategy which is order optimal in the sense that it finds a maximum matching size which is consistent with a given matching priority. The strategy is based on an order-optimal matching algorithm originally proposed in connection with assignment problems by D. Gale. When a matching priority is given, the algorithm ensures that the discarded units have the lowest possible matching priority. We discuss the algorithm’s complexity and its relation with classic optimal matching. We illustrate its use with a problem in a case study concerning a comparison of female and male executives and a simulation.
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Wang, Xia, Tianlong Ma, Jun Yin, and Chengfu Ye. "Fractional matching preclusion for radix triangular mesh." Discrete Mathematics, Algorithms and Applications 11, no. 04 (August 2019): 1950048. http://dx.doi.org/10.1142/s1793830919500484.

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The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost perfect matchings. As a generalization, Liu and Liu recently introduced the concept of fractional matching preclusion number. The fractional matching preclusion number (FMP number) of [Formula: see text], denoted by [Formula: see text], is the minimum number of edges whose deletion leaves the resulting graph without a fractional perfect matching. The fractional strong matching preclusion number (FSMP number) of [Formula: see text], denoted by [Formula: see text], is the minimum number of vertices and edges whose deletion leaves the resulting graph without a fractional perfect matching. In this paper, we study the fractional matching preclusion number and the fractional strong matching preclusion number for the radix triangular mesh [Formula: see text], and all the optimal fractional matching preclusion sets and fractional strong matching preclusion sets of these graphs are categorized.
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Anantapantula, Sai, Christopher Melekian, and Eddie Cheng. "Matching Preclusion for the Shuffle-Cubes." Parallel Processing Letters 28, no. 03 (September 2018): 1850012. http://dx.doi.org/10.1142/s0129626418500123.

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The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost perfect matchings. A graph is maximally matched if its matching preclusion number is equal to its minimum degree, and is super matched if the matching preclusion number can only be achieved by deleting all edges incident to a single vertex. In this paper, we determine the matching preclusion number and classify the optimal matching preclusion sets for the shuffle-cube graphs, a variant of the well-known hypercubes.
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CHENG, EDDIE, RANDY JIA, and DAVID LU. "MATCHING PRECLUSION AND CONDITIONAL MATCHING PRECLUSION FOR AUGMENTED CUBES." Journal of Interconnection Networks 11, no. 01n02 (March 2010): 35–60. http://dx.doi.org/10.1142/s0219265910002726.

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The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost-perfect matchings. For many interconnection networks, the optimal sets are precisely those incident to a single vertex. Recently, the conditional matching preclusion number of a graph was introduced to look for obstruction sets beyond those incident to a single vertex. It is defined to be the minimum number of edges whose deletion results in a graph with no isolated vertices that has neither perfect matchings nor almost-perfect matchings. In this paper, we find this number and classify all optimal sets for the augmented cubes, a class of networks designed as an improvement of the hypercubes.
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Ma, Tianlong, Yaping Mao, Eddie Cheng, and Jinling Wang. "Fractional Matching Preclusion for (n, k)-Star Graphs." Parallel Processing Letters 28, no. 04 (December 2018): 1850017. http://dx.doi.org/10.1142/s0129626418500172.

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The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost perfect matchings. As a generalization, Liu and Liu introduced the concept of fractional matching preclusion number in 2017. The Fractional Matching Preclusion Number (FMP number) of G is the minimum number of edges whose deletion leaves the resulting graph without a fractional perfect matching. The Fractional Strong Matching Preclusion Number (FSMP number) of G is the minimum number of vertices and/or edges whose deletion leaves the resulting graph without a fractional perfect matching. In this paper, we obtain the FMP number and the FSMP number for (n, k)-star graphs. In addition, all the optimal fractional strong matching preclusion sets of these graphs are categorized.
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Dissertations / Theses on the topic "Matching"

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Ranger, Martin. "Matching issues." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2690.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.<br>Thesis research directed by: Economics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Prakash, Piyush Martin Alain J. "Slack matching /." Diss., Pasadena, Calif. : California Institute of Technology, 2005. http://resolver.caltech.edu/CaltechETD:etd-05272005-134017.

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Jin, Wei. "GRAPH PATTERN MATCHING, APPROXIMATE MATCHING AND DYNAMIC GRAPH INDEXING." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1307547974.

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Tam, Siu-lung. "Linear-size indexes for approximate pattern matching and dictionary matching." Click to view the E-thesis via HKUTO, 2010. http://sunzi.lib.hku.hk/hkuto/record/B44205326.

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Tam, Siu-lung, and 譚小龍. "Linear-size indexes for approximate pattern matching and dictionary matching." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44205326.

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Ko, E. Soon. "Product Matching through Multimodal Image and Text Combined Similarity Matching." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301306.

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Product matching in e-commerce is an area that faces more and more challenges with growth in the e-commerce marketplace as well as variation in the quality of data available online for each product. Product matching for e-commerce provides competitive possibilities for vendors and flexibility for customers by identifying identical products from different sources. Traditional methods in product matching are often conducted through rule-based methods and methods tackling the issue through machine learning usually do so through unimodal systems. Moreover, existing methods would tackle the issue through product identifiers which are not always unified for each product. This thesis provides multimodal approaches through product name, description, and image to the problem area of product matching that outperforms unimodal approaches. Three multimodal approaches were taken, one unsupervised and two supervised. The unsupervised approach uses straight-forward embedding space to nearest neighbor search that provides better results than unimodal approaches. One of the supervised multimodal approaches uses Siamese network on the embedding space which outperforms the unsupervised multi- modal approach. Finally, the last supervised approach instead tackles the issue by exploiting distance differences in each modality through logistic regression and a decision system that provided the best results.<br>Produktmatchning inom e-handel är ett område som möter fler och fler utmaningar med hänsyn till den tillväxt som e-handelsmarknaden undergått och fortfarande undergår samt variation i kvaliteten på den data som finns tillgänglig online för varje produkt. Produktmatchning inom e-handel är ett område som ger konkurrenskraftiga möjligheter för leverantörer och flexibilitet för kunder genom att identifiera identiska produkter från olika källor. Traditionella metoder för produktmatchning genomfördes oftast genom regelbaserade metoder och metoder som utnyttjar maskininlärning gör det vanligtvis genom unimodala system. Dessutom utnyttjar mestadels av befintliga metoder produktidentifierare som inte alltid är enhetliga för varje produkt mellan olika källor. Denna studie ger istället förslag till multimodala tillvägagångssätt som istället använder sig av produktnamn, produktbeskrivning och produktbild för produktmatchnings-problem vilket ger bättre resultat än unimodala metoder. Tre multimodala tillvägagångssätt togs, en unsupervised och två supervised. Den unsupervised metoden använder embeddings vektorerna rakt av för att göra en nearest neighborsökning vilket gav bättre resultat än unimodala tillvägagångssätt. Ena supervised multimodal tillvägagångssätten använder siamesiska nätverk på embedding utrymmet vilket gav resultat som överträffade den unsupervised multimodala tillvägagångssättet. Slutligen tar den sista supervised metoden istället avståndsskillnader i varje modalitet genom logistisk regression och ett beslutssystem som gav bästa resultaten.
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Karichery, Sureshan. "Sequential matching problem." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=971627754.

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Michalis, Konstantinos. "Background matching camouflage." Thesis, University of Bristol, 2017. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.723478.

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Neou, Both Emerite. "Permutation pattern matching." Thesis, Paris Est, 2017. http://www.theses.fr/2017PESC1239/document.

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Cette thèse s'intéresse au problème de la recherche de motif dans les permutations, qui a pour objectif de savoir si un motif apparaît dans un texte, en prenant en compte que le motif et le texte sont des permutations. C'est-à-dire s'il existe des éléments du texte tel que ces éléments sont triés de la même manière et apparaissent dans le même ordre que les éléments du motif. Ce problème est NP complet. Cette thèse expose des cas particuliers de ce problème qui sont solvable en temps polynomial.Pour cela nous étudions le problème en donnant des contraintes sur le texte et/ou le motif. En particulier, le cas où le texte et/ou le motif sont des permutations qui ne contiennent pas les motifs 2413 et 3142 (appelé permutation séparable) et le cas où le texte et/ou le motif sont des permutations qui ne contiennent pas les motifs 213 et 231 sont considérés. Des problèmes dérivés de la recherche de motif et le problème de la recherche de motif bivinculaire sont aussi étudiés<br>This thesis focuses on permutation pattern matching problem, which askswhether a pattern occurs in a text where both the pattern and text are permutations.In other words, we seek to determine whether there exist elements ofthe text such that they are sorted and appear in the same order as the elementsof the pattern. The problem is NP-complete. This thesis examines particularcases of the problem that are polynomial-time solvable.For this purpose, we study the problem by giving constraints on the permutationstext and/or pattern. In particular, the cases in which the text and/orpattern are permutations in which the patterns 2413 and 3142 do not occur(also known as separable permutations) and in which the text and/or patternare permutations in which the patterns 213 and 231 do not occur (also known aswedge permutations) are also considered. Some problems related to the patternmatching and the permutation pattern matching with bivincular pattern arealso studied
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Modi, Amit. "Matching Based Diversity." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1306866934.

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Books on the topic "Matching"

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Istar, Schwager, Siede George, and Preis Donna, eds. Matching. London: Evans Bros., 1994.

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Siede, George. Matching. Lincolnwood, Ill: Publications International, 1993.

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(Firm), Clever Factory, and Cuddly Duck Productions, eds. Turn & learn matching. Nashville, TN: Clever Factory, 2007.

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Lovász, László. Matching theory. Budapest: Akadémiai Kiadó, 1986.

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Lovász, L. Matching theory. Amsterdam: North-Holland, 1986.

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Vosselman, G. Relational matching. Berlin: Springer-Verlag, 1992.

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Mortensen, Dale. Island matching. Cambridge, MA: National Bureau of Economic Research, 2007.

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D'Orazio, Marcello, Marco Di Zio, and Mauro Scanu. Statistical Matching. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470023554.

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Rässler, Susanne. Statistical Matching. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0053-3.

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Vosselman, G., ed. Relational Matching. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55798-9.

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Book chapters on the topic "Matching"

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Kleinbaum, David G., Kevin M. Sullivan, and Nancy D. Barker. "Matching." In ActivEpi Companion Textbook, 477–515. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5428-1_15.

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Gerth, Christian. "Matching." In Business Process Models. Change Management, 51–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38604-6_4.

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Kozen, Dexter C. "Matching." In The Design and Analysis of Algorithms, 101–5. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-4400-4_19.

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French, Michael J. "Matching." In Conceptual Design for Engineers, 117–42. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-3627-9_5.

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Eglese, Richard W. "Matching." In Encyclopedia of Operations Research and Management Science, 490–92. New York, NY: Springer US, 2001. http://dx.doi.org/10.1007/1-4020-0611-x_589.

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Allen, Peter K. "Matching." In The Kluwer International Series in Engineering and Computer Science, 95–108. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-2005-0_7.

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Davison, Michael. "Matching." In Encyclopedia of the Sciences of Learning, 2100–2104. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_484.

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Hochstättler, Winfried, and Alexander Schliep. "Matching." In CATBox, 111–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03822-8_8.

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Nemhauser, George, and Laurence Wolsey. "Matching." In Integer and Combinatorial Optimization, 608–58. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118627372.ch15.

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Weik, Martin H. "matching." In Computer Science and Communications Dictionary, 983. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_11157.

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Conference papers on the topic "Matching"

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Huo, Jiayu, Ruiqiang Xiao, Haotian Zheng, Yang Liu, Sébastien Ourselin, and Rachel Sparks. "MatchSeg: Towards Better Segmentation via Reference Image Matching." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2068–73. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822622.

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Rafa Islam, Khandoker N., Xin Zan, and David J. Perreault. "Controllable Transformation Matching Networks for Efficient RF Impedance Matching." In 2024 IEEE Workshop on Control and Modeling for Power Electronics (COMPEL), 1–8. IEEE, 2024. http://dx.doi.org/10.1109/compel57542.2024.10613972.

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Bacso, Gabor, Anita Keszler, and Zsolt Tuza. "Matching Matchings." In 2013 3rd Eastern European Regional Conference on the Engineering of Computer Based Systems (ECBS-EERC). IEEE, 2013. http://dx.doi.org/10.1109/ecbs-eerc.2013.19.

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Jandaghi, Pegah, and Jay Pujara. "Identifying Quantifiably Verifiable Statements from Text." In Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.matching-1.2.

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Baek, Jinheon, Alham Aji, and Amir Saffari. "Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering." In Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.matching-1.7.

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Schumacher, Elliot, James Mayfield, and Mark Dredze. "On the Surprising Effectiveness of Name Matching Alone in Autoregressive Entity Linking." In Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.matching-1.6.

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Ramanan, Karthik. "Corpus-Based Task-Specific Relation Discovery." In Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.matching-1.5.

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Yuan, Jiaqing, Michele Merler, Mihir Choudhury, Raju Pavuluri, Munindar Singh, and Maja Vukovic. "CoSiNES: Contrastive Siamese Network for Entity Standardization." In Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.matching-1.9.

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Wold, Sondre, Lilja Øvrelid, and Erik Velldal. "Text-To-KG Alignment: Comparing Current Methods on Classification Tasks." In Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.matching-1.1.

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Chen, Lihu, Simon Razniewski, and Gerhard Weikum. "Knowledge Base Completion for Long-Tail Entities." In Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.matching-1.8.

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Reports on the topic "Matching"

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Mortensen, Dale. Island Matching. Cambridge, MA: National Bureau of Economic Research, August 2007. http://dx.doi.org/10.3386/w13287.

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Margolius, Barbara. Derivative Matching Game. Washington, DC: The MAA Mathematical Sciences Digital Library, July 2008. http://dx.doi.org/10.4169/loci002651.

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Dauth, Wolfgang, Sebastian Findeisen, Enrico Moretti, and Jens Suedekum. Matching in Cities. Cambridge, MA: National Bureau of Economic Research, November 2018. http://dx.doi.org/10.3386/w25227.

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Schwarz, Michael, and M. Bumin Yenmez. Median Stable Matching. Cambridge, MA: National Bureau of Economic Research, January 2009. http://dx.doi.org/10.3386/w14689.

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Lange, Fabian, and Theodore Papageorgiou. Beyond Cobb-Douglas: Flexibly Estimating Matching Functions with Unobserved Matching Efficiency. Cambridge, MA: National Bureau of Economic Research, April 2020. http://dx.doi.org/10.3386/w26972.

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Duffie, Darrell, Lei Qiao, and Yeneng Sun. Dynamic Directed Random Matching. Cambridge, MA: National Bureau of Economic Research, November 2015. http://dx.doi.org/10.3386/w21731.

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Diamond, Peter, and Ayşegül Şahin. Disaggregating the Matching Function. Cambridge, MA: National Bureau of Economic Research, December 2016. http://dx.doi.org/10.3386/w22965.

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Marriner, John, and /Fermilab. Phase Space Matching Errors. Office of Scientific and Technical Information (OSTI), March 1994. http://dx.doi.org/10.2172/984595.

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Phillips, A., and M. Davis. Matching of Language Tags. RFC Editor, September 2006. http://dx.doi.org/10.17487/rfc4647.

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Tsoukalas, L. DOE Matching Grant Program. Office of Scientific and Technical Information (OSTI), December 2002. http://dx.doi.org/10.2172/836053.

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