Search (3 results, page 1 of 1)

  • × author_ss:"Gonçalves, M.A."
  • × year_i:[2010 TO 2020}
  1. Dalip, D.H.; Gonçalves, M.A.; Cristo, M.; Calado, P.: ¬A general multiview framework for assessing the quality of collaboratively created content on web 2.0 (2017) 0.11 
    0.106065564 = product of:
  0.21213113 = sum of:
    0.16976728 = weight(_text_:assess in 3343) [ClassicSimilarity], result of:
      0.16976728 = score(doc=3343,freq=4.0), product of:
        0.36863554 = queryWeight, product of:
          5.8947687 = idf(docFreq=330, maxDocs=44218)
          0.062536046 = queryNorm
        0.4605288 = fieldWeight in 3343, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.8947687 = idf(docFreq=330, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3343)
    0.042363856 = weight(_text_:22 in 3343) [ClassicSimilarity], result of:
      0.042363856 = score(doc=3343,freq=2.0), product of:
        0.21899058 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.062536046 = queryNorm
        0.19345059 = fieldWeight in 3343, 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=3343)
  0.5 = coord(2/4)
    Abstract
    User-generated content is one of the most interesting phenomena of current published media, as users are now able not only to consume, but also to produce content in a much faster and easier manner. However, such freedom also carries concerns about content quality. In this work, we propose an automatic framework to assess the quality of collaboratively generated content. Quality is addressed as a multidimensional concept, modeled as a combination of independent assessments, each regarding different quality dimensions. Accordingly, we adopt a machine-learning (ML)-based multiview approach to assess content quality. We perform a thorough analysis of our framework on two different domains: Questions and Answer Forums and Collaborative Encyclopedias. This allowed us to better understand when and how the proposed multiview approach is able to provide accurate quality assessments. Our main contributions are: (a) a general ML multiview framework that takes advantage of different views of quality indicators; (b) the improvement (up to 30%) in quality assessment over the best state-of-the-art baseline methods; (c) a thorough feature and view analysis regarding impact, informativeness, and correlation, based on two distinct domains.
    Date
    16.11.2017 13:04:22
  2. Silva, R.M.; Gonçalves, M.A.; Veloso, A.: ¬A Two-stage active learning method for learning to rank (2014) 0.03 
    0.030010901 = product of:
  0.120043606 = sum of:
    0.120043606 = weight(_text_:assess in 1184) [ClassicSimilarity], result of:
      0.120043606 = score(doc=1184,freq=2.0), product of:
        0.36863554 = queryWeight, product of:
          5.8947687 = idf(docFreq=330, maxDocs=44218)
          0.062536046 = queryNorm
        0.32564306 = fieldWeight in 1184, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.8947687 = idf(docFreq=330, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1184)
  0.25 = coord(1/4)
    Abstract
    Learning to rank (L2R) algorithms use a labeled training set to generate a ranking model that can later be used to rank new query results. These training sets are costly and laborious to produce, requiring human annotators to assess the relevance or order of the documents in relation to a query. Active learning algorithms are able to reduce the labeling effort by selectively sampling an unlabeled set and choosing data instances that maximize a learning function's effectiveness. In this article, we propose a novel two-stage active learning method for L2R that combines and exploits interesting properties of its constituent parts, thus being effective and practical. In the first stage, an association rule active sampling algorithm is used to select a very small but effective initial training set. In the second stage, a query-by-committee strategy trained with the first-stage set is used to iteratively select more examples until a preset labeling budget is met or a target effectiveness is achieved. We test our method with various LETOR benchmarking data sets and compare it with several baselines to show that it achieves good results using only a small portion of the original training sets.
  3. Belém, F.M.; Almeida, J.M.; Gonçalves, M.A.: ¬A survey on tag recommendation methods : a review (2017) 0.01 
    0.010590964 = product of:
  0.042363856 = sum of:
    0.042363856 = weight(_text_:22 in 3524) [ClassicSimilarity], result of:
      0.042363856 = score(doc=3524,freq=2.0), product of:
        0.21899058 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.062536046 = queryNorm
        0.19345059 = fieldWeight in 3524, 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=3524)
  0.25 = coord(1/4)
    Date
    16.11.2017 13:30:22

Authors

Themes