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Sperber, W.; Schöneberg, U.: Machine-learning methods for classification and content authority control in mathematics (2015)
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- Abstract
- The abstracting and reviewing service zbMATH (zbMATH, 1931- ) is the most comprehensive bibliographic database of mathematical literature. The database uses reviews, keywords and classification for content analysis of mathematical publications. Controlled vocabularies and classification schemes are important for a uniform and standardised analysis of the content and precise information retrieval. Over the last few years, the zbMATH team has started developing machine-based concepts and tools to create controlled vocabularies and to improve the Mathematics Subject Classification (MSC) scheme. Concepts of natural language processing and other machine learning methods, especially neural networks, were adapted to the specific requirements of mathematical information, e.g., named mathematical entities and mathematical formulas. The tools are used for key phrase extraction and classification of mathematical publications. Basing on the extracted key phrases, a prototype for a controlled vocabulary for mathematics was created. The tools and the state of the art are described briefly. These activities will help - in cooperation with authority control for authors, series and institutions - to automate the zbMATH workflow and improve the usefulness and information retrieval capabilities of the database.
- Content
- Präsentation für The International UDC Seminar entitled "Classification & Authority Control: Expanding Resource Discovery" took place in the National Library of Portugal in Lisbon, on 29-30 October 2015. Vgl.: http://www.udcds.com/seminar/2015/media/slides/Sperber_InternationalUDCSeminar2015.pdf.
- Source
- Classification and authority control: expanding resource discovery: proceedings of the International UDC Seminar 2015, 29-30 October 2015, Lisbon, Portugal. Eds.: Slavic, A. u. M.I. Cordeiro
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Schöneberg, U.; Sperber, W.: ¬The DeLiVerMATH project : text analysis in mathematics (2013)
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- 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.
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Schöneberg, U.; Gödert, W.: Erschließung mathematischer Publikationen mittels linguistischer Verfahren (2012)
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- Abstract
- Die Zahl der mathematik-relevanten Publikationn steigt von Jahr zu Jahr an. Referatedienste wie da Zentralblatt MATH und Mathematical Reviews erfassen die bibliographischen Daten, erschließen die Arbeiten inhaltlich und machen sie - heute über Datenbanken, früher in gedruckter Form - für den Nutzer suchbar. Keywords sind ein wesentlicher Bestandteil der inhaltlichen Erschließung der Publikationen. Keywords sind meist keine einzelnen Wörter, sondern Mehrwortphrasen. Das legt die Anwendung linguistischer Methoden und Verfahren nahe. Die an der FH Köln entwickelte Software 'Lingo' wurde für die speziellen Anforderungen mathematischer Texte angepasst und sowohl zum Aufbau eines kontrollierten Vokabulars als auch zur Extraction von Keywords aus mathematischen Publikationen genutzt. Es ist geplant, über eine Verknüpfung von kontrolliertem Vokabular und der Mathematical Subject Classification Methoden für die automatische Klassifikation für den Referatedienst Zentralblatt MATH zu entwickeln und zu erproben.
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Schöneberg, U.; Sperber, W.: POS tagging and its applications for mathematics (2014)
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- Abstract
- Content analysis of scientific publications is a nontrivial task, but a useful and important one for scientific information services. In the Gutenberg era it was a domain of human experts; in the digital age many machine-based methods, e.g., graph analysis tools and machine-learning techniques, have been developed for it. Natural Language Processing (NLP) is a powerful machine-learning approach to semiautomatic speech and language processing, which is also applicable to mathematics. The well established methods of NLP have to be adjusted for the special needs of mathematics, in particular for handling mathematical formulae. We demonstrate a mathematics-aware part of speech tagger and give a short overview about our adaptation of NLP methods for mathematical publications. We show the use of the tools developed for key phrase extraction and classification in the database zbMATH.
