Automated Classification and Categorization of Mathematical Knowledge
ŘEHŮŘEK, Radim and Petr SOJKA. Automated Classification and Categorization of Mathematical Knowledge. In Intelligent Computer Mathematics: AISC/Calculemus/MKM LNAI 5144. první. Berlin, Heidelberg, New York: Springer-Verlag, 2008, p. 543-557. ISBN 978-3-540--85109-7. |
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Basic information | |
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Original name | Automated Classification and Categorization of Mathematical Knowledge |
Name in Czech | Automatická klasifikace a kategorizace matematiky |
Authors | ŘEHŮŘEK, Radim (203 Czech Republic) and Petr SOJKA (203 Czech Republic, guarantor). |
Edition | první. Berlin, Heidelberg, New York, Intelligent Computer Mathematics: AISC/Calculemus/MKM LNAI 5144, p. 543-557, 15 pp. 2008. |
Publisher | Springer-Verlag |
Other information | |
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Original language | English |
Type of outcome | Proceedings paper |
Field of Study | 10201 Computer sciences, information science, bioinformatics |
Country of publisher | United Kingdom of Great Britain and Northern Ireland |
Confidentiality degree | is not subject to a state or trade secret |
WWW | web of conference paper preprint Springer Link ACM Portal |
Impact factor | Impact factor: 0.402 in 2005 |
RIV identification code | RIV/00216224:14330/08:00024245 |
Organization unit | Faculty of Informatics |
ISBN | 978-3-540--85109-7 |
ISSN | 0302-9743 |
UT WoS | 000258392600043 |
Keywords (in Czech) | strojové učení; klasifikace; kategorizace; podobnost matematických dokumentů; správa matematických znalostí |
Keywords in English | machine learning; classification; categorization; similarity of mathematical papers; mathematical knowledge management; MSC;mathematical subject classification |
Tags | categorization, CLASSIFICATION, machine learning, mathematical knowledge management, mathematical subject classification, MSC, similarity of mathematical papers |
Tags | International impact, Reviewed |
Changed by | Changed by: doc. RNDr. Petr Sojka, Ph.D., učo 2378. Changed: 22/6/2009 13:22. |
Abstract |
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There is a common Mathematics Subject Classification (MSC) System used for categorizing mathematical papers and knowledge. We present results of machine learning of the MSC on full texts of papers in the mathematical digital libraries DML-CZ and NUMDAM. The F1-measure achieved on classification task of top-level MSC categories exceeds 89%. We describe and evaluate our methods for measuring the similarity. of papers in the digital library based on paper full texts. |
Abstract (in Czech) |
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Existuje široce používaný systém Mathematics Subject Classification (MSC) pro kategorizaci a vyhledávání matematických článků a textů. Preyentujeme výsledky strojového učení MSC z článků digitálních knihoven DML-CZ a NUMDAM. Míra F1 získaná při vyhodnocení klasifikace hlavních MSC kategorií přesahuje 89%. Popisujeme a vyhodnocujeme naše metody pro měření podobnosti. |
Links | |
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LC536, research and development project | Name: Centrum komputační lingvistiky |
Investor: Ministry of Education, Youth and Sports of the CR, Centrum komputační lingvistiky | |
1ET200190513, research and development project | Name: DML-CZ: Česká digitální matematická knihovna |
Investor: Academy of Sciences of the Czech Republic, DML-CZ: Czech Digital Mathematical Library | |
1ET208050401, research and development project | Name: E-learning v kontextu sémantického webu |
Investor: Academy of Sciences of the Czech Republic, E-learning in the Semantic Web Context | |
2C06009, research and development project | Name: Prostředky tvorby komplexní báze znalostí pro komunikaci se sémantickým webem v přirozeném jazyce (Acronym: COT-SEWing) |
Investor: Ministry of Education, Youth and Sports of the CR |
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