Search (1 results, page 1 of 1)

  • × author_ss:"Allen, K."
  • × theme_ss:"Automatisches Abstracting"
  • × type_ss:"el"
  1. Gomez, J.; Allen, K.; Matney, M.; Awopetu, T.; Shafer, S.: Experimenting with a machine generated annotations pipeline (2020) 0.00
    3.3297235E-4 = product of:
      0.006659447 = sum of:
        0.006659447 = weight(_text_:in in 657) [ClassicSimilarity], result of:
          0.006659447 = score(doc=657,freq=4.0), product of:
            0.039165888 = queryWeight, product of:
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.02879306 = queryNorm
            0.17003182 = fieldWeight in 657, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.0625 = fieldNorm(doc=657)
      0.05 = coord(1/20)
    
    Abstract
    The UCLA Library reorganized its software developers into focused subteams with one, the Labs Team, dedicated to conducting experiments. In this article we describe our first attempt at conducting a software development experiment, in which we attempted to improve our digital library's search results with metadata from cloud-based image tagging services. We explore the findings and discuss the lessons learned from our first attempt at running an experiment.