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Association Analyzer Implementation: State of the Art: Deliverable 8.1 of project EuDML

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LEE, Mark, Petr SOJKA, Volker SORGE, Josef BAKER, Wojtek HURY and Łukasz BOLIKOWSKI. Association Analyzer Implementation: State of the Art: Deliverable 8.1 of project EuDML. 1 as of 27th November 2010. : EU CIP-ICT-PSP project 250503 EuDML: The European Digital Mathematics Library, 2010. 22 pp. Deliverable D8.1.
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Basic information
Original name Association Analyzer Implementation: State of the Art: Deliverable 8.1 of project EuDML
Authors LEE, Mark (826 United Kingdom), Petr SOJKA (203 Czech Republic, guarantor, belonging to the institution), Volker SORGE (826 United Kingdom), Josef BAKER (826 United Kingdom), Wojtek HURY (616 Poland) and Łukasz BOLIKOWSKI (616 Poland).
Edition 1 as of 27th November 2010. 22 pp. Deliverable D8.1, 2010.
Publisher EU CIP-ICT-PSP project 250503 EuDML: The European Digital Mathematics Library
Other information
Original language English
Type of outcome special-purpose publication
Field of Study Informatics
Country of publisher United Kingdom
Confidentiality degree is not subject to a state or trade secret
WWW Fulltext
RIV identification code RIV/00216224:14330/10:00062172
Organization unit Faculty of Informatics
Keywords in English The European Digital Mathematics Library; EuDML; gensim; citation linking; crossref; citation matching; document clustering; identity discovery
Tags International impact, Reviewed
Changed by Changed by: doc. RNDr. Petr Sojka, Ph.D., učo 2378. Changed: 5. 12. 2012 18:15.
Abstract
This report focuses on two key technologies: Citation Indexing and Document Clustering. Citation Indexing concerns the automatic parsing and linking of citations to create a network of documents within the collection. This technology is well established in digital libraries and searchable archives such as CiteSeerX, Google Scholar, general projects as DRIVER, and mathematical specific digital libraries such as NUMDAM, DML-CZ or referative databases Zentralblatt MATH and Mathematical Reviews. Document Classification and Clustering are also established technologies within Information Retrieval but have not to date been widely used within digital libraries. In particular, there is very little previous work applying classification and clustering techniques to mathematical documents. However, initial research appears promising and we believe that the addition of these technologies will allow facilities beyond the current state of the art.
Links
250503, internal MU codeName: The European Digital Mathematics Library (Acronym: EuDML)
Investor: European Union, Competitiveness and inovation framework programme
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