Search (11 results, page 1 of 1)

0.008037162 = product of:
  0.07233446 = sum of:
    0.07233446 = weight(_text_:germany in 3105) [ClassicSimilarity], result of:
      0.07233446 = score(doc=3105,freq=2.0), product of:
        0.21956629 = queryWeight, product of:
          5.963546 = idf(docFreq=308, maxDocs=44218)
          0.036818076 = queryNorm
        0.32944247 = fieldWeight in 3105, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.963546 = idf(docFreq=308, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3105)
  0.11111111 = coord(1/9)

Source

Proceedings of the 15th European Networked Knowledge Organization Systems Workshop (NKOS 2016) co-located with the 20th International Conference on Theory and Practice of Digital Libraries 2016 (TPDL 2016), Hannover, Germany, September 9, 2016. Edi. by Philipp Mayr et al. [http://ceur-ws.org/Vol-1676/=urn:nbn:de:0074-1676-5]

0.0072904974 = product of:
  0.03280724 = sum of:
    0.020336384 = weight(_text_:data in 3780) [ClassicSimilarity], result of:
      0.020336384 = score(doc=3780,freq=2.0), product of:
        0.11642061 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.036818076 = queryNorm
        0.17468026 = fieldWeight in 3780, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3780)
    0.012470853 = product of:
      0.024941705 = sum of:
        0.024941705 = weight(_text_:22 in 3780) [ClassicSimilarity], result of:
          0.024941705 = score(doc=3780,freq=2.0), product of:
            0.12893063 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.036818076 = queryNorm
            0.19345059 = fieldWeight in 3780, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3780)
      0.5 = coord(1/2)
  0.22222222 = coord(2/9)

Abstract

Wir leben im 21. Jahrhundert, und vieles, was vor hundert und noch vor fünfzig Jahren als Science Fiction abgetan worden wäre, ist mittlerweile Realität. Raumsonden fliegen zum Mars, machen dort Experimente und liefern Daten zur Erde zurück. Roboter werden für Routineaufgaben eingesetzt, zum Beispiel in der Industrie oder in der Medizin. Digitalisierung, künstliche Intelligenz und automatisierte Verfahren sind kaum mehr aus unserem Alltag wegzudenken. Grundlage vieler Prozesse sind lernende Algorithmen. Die fortschreitende digitale Transformation ist global und umfasst alle Lebens- und Arbeitsbereiche: Wirtschaft, Gesellschaft und Politik. Sie eröffnet neue Möglichkeiten, von denen auch Bibliotheken profitieren. Der starke Anstieg digitaler Publikationen, die einen wichtigen und prozentual immer größer werdenden Teil des Kulturerbes darstellen, sollte für Bibliotheken Anlass sein, diese Möglichkeiten aktiv aufzugreifen und einzusetzen. Die Auswertbarkeit digitaler Inhalte, beispielsweise durch Text- and Data-Mining (TDM), und die Entwicklung technischer Verfahren, mittels derer Inhalte miteinander vernetzt und semantisch in Beziehung gesetzt werden können, bieten Raum, auch bibliothekarische Erschließungsverfahren neu zu denken. Daher beschäftigt sich die Deutsche Nationalbibliothek (DNB) seit einigen Jahren mit der Frage, wie sich die Prozesse bei der Erschließung von Medienwerken verbessern und maschinell unterstützen lassen. Sie steht dabei im regelmäßigen kollegialen Austausch mit anderen Bibliotheken, die sich ebenfalls aktiv mit dieser Fragestellung befassen, sowie mit europäischen Nationalbibliotheken, die ihrerseits Interesse an dem Thema und den Erfahrungen der DNB haben. Als Nationalbibliothek mit umfangreichen Beständen an digitalen Publikationen hat die DNB auch Expertise bei der digitalen Langzeitarchivierung aufgebaut und ist im Netzwerk ihrer Partner als kompetente Gesprächspartnerin geschätzt.

Date

19. 8.2017 9:24:22

0.006261982 = product of:
  0.05635784 = sum of:
    0.05635784 = weight(_text_:data in 3726) [ClassicSimilarity], result of:
      0.05635784 = score(doc=3726,freq=6.0), product of:
        0.11642061 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.036818076 = queryNorm
        0.48408815 = fieldWeight in 3726, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0625 = fieldNorm(doc=3726)
  0.11111111 = coord(1/9)

Abstract

Die Arbeit mit unstrukturierten Daten dient gerne als Paradebeispiel für Big Data, weil die technologischen Möglichkeiten das Speichern und Verarbeiten großer Datenmengen erlauben und die Mehrheit dieser Daten unstrukturiert ist. Allerdings ist im Zusammenhang mit unstrukturierten Daten meist von der Analyse und der Extraktion von Informationen aus Texten die Rede. Viel weniger hingegen wird das Thema der Bildanalyse thematisiert. Diese gilt aber nach wie vor als eine Königdisziplin der modernen Computerwissenschaft.

Source

https://jaxenter.de/big-data-bildanalyse-50313

0.004473776 = product of:
  0.040263984 = sum of:
    0.040263984 = weight(_text_:data in 2933) [ClassicSimilarity], result of:
      0.040263984 = score(doc=2933,freq=4.0), product of:
        0.11642061 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.036818076 = queryNorm
        0.34584928 = fieldWeight in 2933, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2933)
  0.11111111 = coord(1/9)

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.

Content

Vortrag, "Semantics, Analytics, Visualisation: Enhancing Scholarly Data Workshop co-located with the 25th International World Wide Web Conference April 11, 2016 - Montreal, Canada", Montreal 2016.

0.0031955543 = product of:
  0.028759988 = sum of:
    0.028759988 = weight(_text_:data in 4309) [ClassicSimilarity], result of:
      0.028759988 = score(doc=4309,freq=4.0), product of:
        0.11642061 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.036818076 = queryNorm
        0.24703519 = fieldWeight in 4309, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4309)
  0.11111111 = coord(1/9)

Abstract

Semantic annotations have to satisfy quality constraints to be useful for digital libraries, which is particularly challenging on large and diverse datasets. Confidence scores of multi-label classification methods typically refer only to the relevance of particular subjects, disregarding indicators of insufficient content representation at the document-level. Therefore, we propose a novel approach that detects documents rather than concepts where quality criteria are met. Our approach uses a deep, multi-layered regression architecture, which comprises a variety of content-based indicators. We evaluated multiple configurations using text collections from law and economics, where the available content is restricted to very short texts. Notably, we demonstrate that the proposed quality estimation technique can determine subsets of the previously unseen data where considerable gains in document-level recall can be achieved, while upholding precision at the same time. Hence, the approach effectively performs a filtering that ensures high data quality standards in operative information retrieval systems.

0.0031634374 = product of:
  0.028470935 = sum of:
    0.028470935 = weight(_text_:data in 2930) [ClassicSimilarity], result of:
      0.028470935 = score(doc=2930,freq=2.0), product of:
        0.11642061 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.036818076 = queryNorm
        0.24455236 = fieldWeight in 2930, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2930)
  0.11111111 = coord(1/9)

Content

Vortrag, "Semantics, Analytics, Visualisation: Enhancing Scholarly Data Workshop co-located with the 25th International World Wide Web Conference April 11, 2016 - Montreal, Canada", Montreal 2016.

0.0031634374 = product of:
  0.028470935 = sum of:
    0.028470935 = weight(_text_:data in 5236) [ClassicSimilarity], result of:
      0.028470935 = score(doc=5236,freq=2.0), product of:
        0.11642061 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.036818076 = queryNorm
        0.24455236 = fieldWeight in 5236, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5236)
  0.11111111 = coord(1/9)

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.

0.0027115175 = product of:
  0.024403658 = sum of:
    0.024403658 = weight(_text_:data in 1167) [ClassicSimilarity], result of:
      0.024403658 = score(doc=1167,freq=2.0), product of:
        0.11642061 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.036818076 = queryNorm
        0.2096163 = fieldWeight in 1167, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=1167)
  0.11111111 = coord(1/9)

0.0027115175 = product of:
  0.024403658 = sum of:
    0.024403658 = weight(_text_:data in 1868) [ClassicSimilarity], result of:
      0.024403658 = score(doc=1868,freq=2.0), product of:
        0.11642061 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.036818076 = queryNorm
        0.2096163 = fieldWeight in 1868, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=1868)
  0.11111111 = coord(1/9)

Abstract

We present a model that generates free-form natural language descriptions of image regions. Our model leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between text and visual data. Our approach is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate the effectiveness of our alignment model with ranking experiments on Flickr8K, Flickr30K and COCO datasets, where we substantially improve on the state of the art. We then show that the sentences created by our generative model outperform retrieval baselines on the three aforementioned datasets and a new dataset of region-level annotations.

0.0027115175 = product of:
  0.024403658 = sum of:
    0.024403658 = weight(_text_:data in 2144) [ClassicSimilarity], result of:
      0.024403658 = score(doc=2144,freq=2.0), product of:
        0.11642061 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.036818076 = queryNorm
        0.2096163 = fieldWeight in 2144, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=2144)
  0.11111111 = coord(1/9)

Abstract

The complexity and diversity of archival resources make constructing rich metadata records time consuming and expensive, which in turn limits access to these valuable materials. However, significant automation of the metadata creation process would dramatically reduce the cost of providing access points, improve access to individual resources, and establish connections between resources that would otherwise remain unknown. Using a case study at Oregon Health & Science University as a lens to examine the conceptual and technical challenges associated with automated extraction of access points, we discuss using publically accessible API's to extract entities (i.e. people, places, concepts, etc.) from digital and digitized objects. We describe why Linked Open Data is not well suited for a use case such as ours. We conclude with recommendations about how this method can be used in archives as well as for other library applications.

0.0022595983 = product of:
  0.020336384 = sum of:
    0.020336384 = weight(_text_:data in 1873) [ClassicSimilarity], result of:
      0.020336384 = score(doc=1873,freq=2.0), product of:
        0.11642061 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.036818076 = queryNorm
        0.17468026 = fieldWeight in 1873, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1873)
  0.11111111 = coord(1/9)

Abstract

Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or "temporally deep", are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and demonstrate the value of these models on benchmark video recognition tasks, image description and retrieval problems, and video narration challenges. In contrast to current models which assume a fixed spatio-temporal receptive field or simple temporal averaging for sequential processing, recurrent convolutional models are "doubly deep" in that they can be compositional in spatial and temporal "layers". Such models may have advantages when target concepts are complex and/or training data are limited. Learning long-term dependencies is possible when nonlinearities are incorporated into the network state updates. Long-term RNN models are appealing in that they directly can map variable-length inputs (e.g., video frames) to variable length outputs (e.g., natural language text) and can model complex temporal dynamics; yet they can be optimized with backpropagation. Our recurrent long-term models are directly connected to modern visual convnet models and can be jointly trained to simultaneously learn temporal dynamics and convolutional perceptual representations. Our results show such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined and/or optimized.