Search (11 results, page 1 of 1)

0.01539102 = product of:
  0.03078204 = sum of:
    0.03078204 = product of:
      0.06156408 = sum of:
        0.06156408 = weight(_text_:22 in 2759) [ClassicSimilarity], result of:
          0.06156408 = score(doc=2759,freq=2.0), product of:
            0.15912095 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045439374 = queryNorm
            0.38690117 = fieldWeight in 2759, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=2759)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

1. 2.2016 18:25:22

0.014283955 = product of:
  0.02856791 = sum of:
    0.02856791 = product of:
      0.05713582 = sum of:
        0.05713582 = weight(_text_:i in 4913) [ClassicSimilarity], result of:
          0.05713582 = score(doc=4913,freq=2.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.33337694 = fieldWeight in 4913, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.0625 = fieldNorm(doc=4913)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

0.012498461 = product of:
  0.024996921 = sum of:
    0.024996921 = product of:
      0.049993843 = sum of:
        0.049993843 = weight(_text_:i in 2933) [ClassicSimilarity], result of:
          0.049993843 = score(doc=2933,freq=2.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.29170483 = fieldWeight in 2933, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2933)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

0.010712966 = product of:
  0.021425933 = sum of:
    0.021425933 = product of:
      0.042851865 = sum of:
        0.042851865 = weight(_text_:i in 3187) [ClassicSimilarity], result of:
          0.042851865 = score(doc=3187,freq=2.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.25003272 = fieldWeight in 3187, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.046875 = fieldNorm(doc=3187)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The quality of stemming algorithms is typically measured in two different ways: (i) how accurately they map the variant forms of a word to the same stem; or (ii) how much improvement they bring to Information Retrieval systems. In this article, we evaluate various stemming algorithms, in four languages, in terms of accuracy and in terms of their aid to Information Retrieval. The aim is to assess whether the most accurate stemmers are also the ones that bring the biggest gain in Information Retrieval. Experiments in English, French, Portuguese, and Spanish show that this is not always the case, as stemmers with higher error rates yield better retrieval quality. As a byproduct, we also identified the most accurate stemmers and the best for Information Retrieval purposes.

0.010712966 = product of:
  0.021425933 = sum of:
    0.021425933 = product of:
      0.042851865 = sum of:
        0.042851865 = weight(_text_:i in 3622) [ClassicSimilarity], result of:
          0.042851865 = score(doc=3622,freq=2.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.25003272 = fieldWeight in 3622, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.046875 = fieldNorm(doc=3622)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

0.009981218 = product of:
  0.019962436 = sum of:
    0.019962436 = product of:
      0.03992487 = sum of:
        0.03992487 = weight(_text_:i in 1875) [ClassicSimilarity], result of:
          0.03992487 = score(doc=1875,freq=10.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.23295423 = fieldWeight in 1875, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.01953125 = fieldNorm(doc=1875)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Content

"Until now, so-called computer vision has largely been limited to recognizing individual objects. The new software, described on Monday by researchers at Google and at Stanford University, teaches itself to identify entire scenes: a group of young men playing Frisbee, for example, or a herd of elephants marching on a grassy plain. The software then writes a caption in English describing the picture. Compared with human observations, the researchers found, the computer-written descriptions are surprisingly accurate. The advances may make it possible to better catalog and search for the billions of images and hours of video available online, which are often poorly described and archived. At the moment, search engines like Google rely largely on written language accompanying an image or video to ascertain what it contains. "I consider the pixel data in images and video to be the dark matter of the Internet," said Fei-Fei Li, director of the Stanford Artificial Intelligence Laboratory, who led the research with Andrej Karpathy, a graduate student. "We are now starting to illuminate it." Dr. Li and Mr. Karpathy published their research as a Stanford University technical report. The Google team published their paper on arXiv.org, an open source site hosted by Cornell University.
In living organisms, webs of neurons in the brain vastly outperform even the best computer-based networks in perception and pattern recognition. But by adopting some of the same architecture, computers are catching up, learning to identify patterns in speech and imagery with increasing accuracy. The advances are apparent to consumers who use Apple's Siri personal assistant, for example, or Google's image search. Both groups of researchers employed similar approaches, weaving together two types of neural networks, one focused on recognizing images and the other on human language. In both cases the researchers trained the software with relatively small sets of digital images that had been annotated with descriptive sentences by humans. After the software programs "learned" to see patterns in the pictures and description, the researchers turned them on previously unseen images. The programs were able to identify objects and actions with roughly double the accuracy of earlier efforts, although still nowhere near human perception capabilities. "I was amazed that even with the small amount of training data that we were able to do so well," said Oriol Vinyals, a Google computer scientist who wrote the paper with Alexander Toshev, Samy Bengio and Dumitru Erhan, members of the Google Brain project. "The field is just starting, and we will see a lot of increases."
Computer vision specialists said that despite the improvements, these software systems had made only limited progress toward the goal of digitally duplicating human vision and, even more elusive, understanding. "I don't know that I would say this is 'understanding' in the sense we want," said John R. Smith, a senior manager at I.B.M.'s T.J. Watson Research Center in Yorktown Heights, N.Y. "I think even the ability to generate language here is very limited." But the Google and Stanford teams said that they expect to see significant increases in accuracy as they improve their software and train these programs with larger sets of annotated images. A research group led by Tamara L. Berg, a computer scientist at the University of North Carolina at Chapel Hill, is training a neural network with one million images annotated by humans. "You're trying to tell the story behind the image," she said. "A natural scene will be very complex, and you want to pick out the most important objects in the image.""

0.008927471 = product of:
  0.017854942 = sum of:
    0.017854942 = product of:
      0.035709884 = sum of:
        0.035709884 = weight(_text_:i in 3232) [ClassicSimilarity], result of:
          0.035709884 = score(doc=3232,freq=2.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.20836058 = fieldWeight in 3232, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3232)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

0.008927471 = product of:
  0.017854942 = sum of:
    0.017854942 = product of:
      0.035709884 = sum of:
        0.035709884 = weight(_text_:i in 3667) [ClassicSimilarity], result of:
          0.035709884 = score(doc=3667,freq=2.0), product of:
            0.17138503 = queryWeight, product of:
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.045439374 = queryNorm
            0.20836058 = fieldWeight in 3667, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.7717297 = idf(docFreq=2765, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3667)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Descriptive metadata play a key role in finding relevant search results in large amounts of unstructured data. However, current scientific audiovisual media are provided with little metadata, which makes them hard to find, let alone individual sequences. In this paper, the TIB / AV-Portal is presented as a use case where methods concerning the automatic generation of metadata, a semantic search and cross-lingual retrieval (German/English) have already been applied. These methods result in a better discoverability of the scientific audiovisual media hosted in the portal. Text, speech, and image content of the video are automatically indexed by specialised GND (Gemeinsame Normdatei) subject headings. A semantic search is established based on properties of the GND ontology. The cross-lingual retrieval uses English 'translations' that were derived by an ontology mapping (DBpedia i. a.). Further ways of increasing the discoverability and reuse of the metadata are publishing them as Linked Open Data and interlinking them with other data sets.

0.0061564078 = product of:
  0.0123128155 = sum of:
    0.0123128155 = product of:
      0.024625631 = sum of:
        0.024625631 = weight(_text_:22 in 1441) [ClassicSimilarity], result of:
          0.024625631 = score(doc=1441,freq=2.0), product of:
            0.15912095 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045439374 = queryNorm
            0.15476047 = fieldWeight in 1441, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=1441)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

0.0061564078 = product of:
  0.0123128155 = sum of:
    0.0123128155 = product of:
      0.024625631 = sum of:
        0.024625631 = weight(_text_:22 in 1442) [ClassicSimilarity], result of:
          0.024625631 = score(doc=1442,freq=2.0), product of:
            0.15912095 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045439374 = queryNorm
            0.15476047 = fieldWeight in 1442, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=1442)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

0.0061564078 = product of:
  0.0123128155 = sum of:
    0.0123128155 = product of:
      0.024625631 = sum of:
        0.024625631 = weight(_text_:22 in 5499) [ClassicSimilarity], result of:
          0.024625631 = score(doc=5499,freq=2.0), product of:
            0.15912095 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045439374 = queryNorm
            0.15476047 = fieldWeight in 5499, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=5499)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

20. 1.2015 18:30:22