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© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 21. Januar 2019)
1Schöneberg, U. ; Sperber, W.: ¬The DeLiVerMATH project : text analysis in mathematics.
In: Intelligent Computer Mathematics: MKM, Calculemus, DML, and Systems and Projects 2013, Held as Part of CICM 2013, Bath, UK, July 8-12, 2013. Proceedings. Eds. J. Carette et al. Berlin : Springer, 2013. S.379-382.
(Lecture notes in computer science; vol. 7961)
Abstract: A high-quality content analysis is essential for retrieval functionalities but the manual extraction of key phrases and classification is expensive. Natural language processing provides a framework to automatize the process. Here, a machine-based approach for the content analysis of mathematical texts is described. A prototype for key phrase extraction and classification of mathematical texts is presented.
Inhalt: Auch als: http://arxiv.org/abs/1306.6944.
Objekt: DeLiVerMATH ; Zentralblatt für Mathematik
2Barthel, S. ; Tönnies, S. ; Balke, W.-T.: Large-scale experiments for mathematical document classification.
In: 15th International Conference on Asia-Pacific Digital Libraries ICADL 2013. Bangalore, India. [to appear, 2013]. Berlin : Springer, 2013. S.xxx-xxx.
Abstract: The ever increasing amount of digitally available information is curse and blessing at the same time. On the one hand, users have increasingly large amounts of information at their fingertips. On the other hand, the assessment and refinement of web search results becomes more and more tiresome and difficult for non-experts in a domain. Therefore, established digital libraries offer specialized collections with a certain degree of quality. This quality can largely be attributed to the great effort invested into semantic enrichment of the provided documents e.g. by annotating their documents with respect to a domain-specific taxonomy. This process is still done manually in many domains, e.g. chemistry CAS, medicine MeSH, or mathematics MSC. But due to the growing amount of data, this manual task gets more and more time consuming and expensive. The only solution for this problem seems to employ automated classification algorithms, but from evaluations done in previous research, conclusions to a real world scenario are difficult to make. We therefore conducted a large scale feasibility study on a real world data set from one of the biggest mathematical digital libraries, i.e. Zentralblatt MATH, with special focus on its practical applicability.
Inhalt: Vgl.: http://www.ifis.cs.tu-bs.de/node/2838.
Themenfeld: Automatisches Klassifizieren
Objekt: Zentralblatt für Mathematik ; MSC ; DeliverMATH