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Automated Classification and Categorization of Mathematical Knowledge

česky | in English

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, 15 pp. ISBN 978-3-540--85109-7.
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Basic information
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
Original language English
Type of outcome article in proceedings
Field of Study Informatics
Country of publisher United Kingdom
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
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)
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
LC536, research and development projectName: Centrum komputační lingvistiky
Investor: Ministry of Education, Youth and Sports of the CR, Basic Research Center
1ET200190513, research and development projectName: DML-CZ: Česká digitální matematická knihovna
Investor: Academy of Sciences of the Czech Republic, Information society (National programme of research)
1ET208050401, research and development projectName: E-learning v kontextu sémantického webu
Investor: Academy of Sciences of the Czech Republic, Information society (National programme of research)
2C06009, research and development projectName: 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, Information technologies for knowledge society
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