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Abstract

The German subject headings authority file (Schlagwortnormdatei/SWD) provides a broad controlled vocabulary for indexing documents of all subjects. Traditionally used for intellectual subject cataloguing primarily of books the Deutsche Nationalbibliothek (DNB, German National Library) has been working on developping and implementing procedures for automated assignment of subject headings for online publications. This project, its results and problems are sketched in the paper.

Content

Beitrag für die Tagung: Beyond libraries - subject metadata in the digital environment and semantic web. IFLA Satellite Post-Conference, 17-18 August 2012, Tallinn. Vgl.: http://http://www.nlib.ee/index.php?id=17763.

Source

Cataloguing & Classification Quarterly 52(2014) no.1, S.102-109

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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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Abstract

We propose a method for improving access to scientific literature by analyzing the content of research papers beyond citation links and topic tracking. Our model relies on a typology of explicit semantic relations. These relations are instantiated in the abstract/introduction part of the papers and can be identified automatically using textual data and external ontologies. Preliminary results show a promising precision in unsupervised relationship classification.

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Content

"People can summarize a complex scene in a few words without thinking twice. It's much more difficult for computers. But we've just gotten a bit closer -- we've developed a machine-learning system that can automatically produce captions (like the three above) to accurately describe images the first time it sees them. This kind of system could eventually help visually impaired people understand pictures, provide alternate text for images in parts of the world where mobile connections are slow, and make it easier for everyone to search on Google for images. Recent research has greatly improved object detection, classification, and labeling. But accurately describing a complex scene requires a deeper representation of what's going on in the scene, capturing how the various objects relate to one another and translating it all into natural-sounding language. Many efforts to construct computer-generated natural descriptions of images propose combining current state-of-the-art techniques in both computer vision and natural language processing to form a complete image description approach. But what if we instead merged recent computer vision and language models into a single jointly trained system, taking an image and directly producing a human readable sequence of words to describe it? This idea comes from recent advances in machine translation between languages, where a Recurrent Neural Network (RNN) transforms, say, a French sentence into a vector representation, and a second RNN uses that vector representation to generate a target sentence in German. Now, what if we replaced that first RNN and its input words with a deep Convolutional Neural Network (CNN) trained to classify objects in images? Normally, the CNN's last layer is used in a final Softmax among known classes of objects, assigning a probability that each object might be in the image. But if we remove that final layer, we can instead feed the CNN's rich encoding of the image into a RNN designed to produce phrases. We can then train the whole system directly on images and their captions, so it maximizes the likelihood that descriptions it produces best match the training descriptions for each image.

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Abstract

The Black Book Interactive Project at the University of Kansas (KU) is developing an expanded corpus of novels by African American authors, with an emphasis on lesser known writers and a goal of expanding research in this field. Using a custom metadata schema with an emphasis on race-related elements, each novel is analyzed for a variety of elements such as literary style, targeted content analysis, historical context, and other areas. Librarians at KU have worked to develop a variety of computational text analysis processes designed to assist with specific aspects of this metadata collection, including text mining and natural language processing, automated subject extraction based on word sense disambiguation, harvesting data from Wikidata, and other actions.

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Date

19. 8.2017 9:24:22