Search (3 results, page 1 of 1)

0.031518865 = product of:
  0.06303773 = sum of:
    0.06303773 = sum of:
      0.031886913 = weight(_text_:b in 3343) [ClassicSimilarity], result of:
        0.031886913 = score(doc=3343,freq=2.0), product of:
          0.1629187 = queryWeight, product of:
            3.542962 = idf(docFreq=3476, maxDocs=44218)
            0.045983754 = queryNorm
          0.19572285 = fieldWeight in 3343, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.542962 = idf(docFreq=3476, maxDocs=44218)
            0.0390625 = fieldNorm(doc=3343)
      0.03115082 = weight(_text_:22 in 3343) [ClassicSimilarity], result of:
        0.03115082 = score(doc=3343,freq=2.0), product of:
          0.16102727 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.045983754 = 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(1/2)

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

0.03087581 = sum of:
  0.014932352 = product of:
    0.07466176 = sum of:
      0.07466176 = weight(_text_:authors in 4921) [ClassicSimilarity], result of:
        0.07466176 = score(doc=4921,freq=4.0), product of:
          0.20963138 = queryWeight, product of:
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.045983754 = queryNorm
          0.35615736 = fieldWeight in 4921, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.0390625 = fieldNorm(doc=4921)
    0.2 = coord(1/5)
  0.015943456 = product of:
    0.031886913 = sum of:
      0.031886913 = weight(_text_:b in 4921) [ClassicSimilarity], result of:
        0.031886913 = score(doc=4921,freq=2.0), product of:
          0.1629187 = queryWeight, product of:
            3.542962 = idf(docFreq=3476, maxDocs=44218)
            0.045983754 = queryNorm
          0.19572285 = fieldWeight in 4921, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.542962 = idf(docFreq=3476, maxDocs=44218)
            0.0390625 = fieldNorm(doc=4921)
    0.5 = coord(1/2)

Abstract

Traditional text-based document classifiers tend to perform poorly an the Web. Text in Web documents is usually noisy and often does not contain enough information to determine their topic. However, the Web provides a different source that can be useful to document classification: its hyperlink structure. In this work, the authors evaluate how the link structure of the Web can be used to determine a measure of similarity appropriate for document classification. They experiment with five different similarity measures and determine their adequacy for predicting the topic of a Web page. Tests performed an a Web directory Show that link information alone allows classifying documents with an average precision of 86%. Further, when combined with a traditional textbased classifier, precision increases to values of up to 90%, representing gains that range from 63 to 132% over the use of text-based classification alone. Because the measures proposed in this article are straightforward to compute, they provide a practical and effective solution for Web classification and related information retrieval tasks. Further, the authors provide an important set of guidelines an how link structure can be used effectively to classify Web documents.

0.007971728 = product of:
  0.015943456 = sum of:
    0.015943456 = product of:
      0.031886913 = sum of:
        0.031886913 = weight(_text_:b in 2531) [ClassicSimilarity], result of:
          0.031886913 = score(doc=2531,freq=2.0), product of:
            0.1629187 = queryWeight, product of:
              3.542962 = idf(docFreq=3476, maxDocs=44218)
              0.045983754 = queryNorm
            0.19572285 = fieldWeight in 2531, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.542962 = idf(docFreq=3476, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2531)
      0.5 = coord(1/2)
  0.5 = coord(1/2)