Search (4 results, page 1 of 1)

0.018318176 = product of:
  0.036636353 = sum of:
    0.036636353 = sum of:
      0.00859806 = weight(_text_:a in 671) [ClassicSimilarity], result of:
        0.00859806 = score(doc=671,freq=16.0), product of:
          0.04772363 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.041389145 = queryNorm
          0.18016359 = fieldWeight in 671, product of:
            4.0 = tf(freq=16.0), with freq of:
              16.0 = termFreq=16.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.0390625 = fieldNorm(doc=671)
      0.028038291 = weight(_text_:22 in 671) [ClassicSimilarity], result of:
        0.028038291 = score(doc=671,freq=2.0), product of:
          0.14493774 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.041389145 = queryNorm
          0.19345059 = fieldWeight in 671, 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=671)
  0.5 = coord(1/2)

Abstract

Although it is commonly expected that the citation context of a reference is likely to provide more detailed and direct information about the nature of a citation, few studies in the literature have specifically addressed the extent to which the information in different parts of a scientific publication differs. Do abstracts tend to use conceptually broader terms than sentences in a citation context in the body of a publication? In this article, we propose a method to analyze and compare latent topics in scientific publications, in particular, from abstracts of papers that cited a target reference and from sentences that cited the target reference. We conducted an experiment and applied topical modeling techniques to full-text papers in eight biomedicine journals. Topics derived from the two sources are compared in terms of their similarities and broad-narrow relationships defined based on information entropy. The results show that abstracts and citation contexts are characterized by distinct sets of topics with moderate overlaps. Furthermore, the results confirm that topics from abstracts of citing papers have broader terms than topics from citation contexts formed by citing sentences. The method and the findings could be used to enhance and extend the current methodologies for research evaluation and citation evaluation.

Date

22. 3.2013 19:50:00

Type

a

0.0016993409 = product of:
  0.0033986818 = sum of:
    0.0033986818 = product of:
      0.0067973635 = sum of:
        0.0067973635 = weight(_text_:a in 3472) [ClassicSimilarity], result of:
          0.0067973635 = score(doc=3472,freq=10.0), product of:
            0.04772363 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.041389145 = queryNorm
            0.14243183 = fieldWeight in 3472, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3472)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The authors address the problem of unsupervised ensemble ranking. Traditional approaches either combine multiple ranking criteria into a unified representation to obtain an overall ranking score or to utilize certain rank fusion or aggregation techniques to combine the ranking results. Beyond the aforementioned combine-then-rank and rank-then-combine approaches, the authors propose a novel rank-learn-combine ranking framework, called Interactive Ranking (iRANK), which allows two base rankers to teach each other before combination during the ranking process by providing their own ranking results as feedback to the others to boost the ranking performance. This mutual ranking refinement process continues until the two base rankers cannot learn from each other any more. The overall performance is improved by the enhancement of the base rankers through the mutual learning mechanism. The authors further design two ranking refinement strategies to efficiently and effectively use the feedback based on reasonable assumptions and rational analysis. Although iRANK is applicable to many applications, as a case study, they apply this framework to the sentence ranking problem in query-focused summarization and evaluate its effectiveness on the DUC 2005 and 2006 data sets. The results are encouraging with consistent and promising improvements.

Type

a

0.0016993409 = product of:
  0.0033986818 = sum of:
    0.0033986818 = product of:
      0.0067973635 = sum of:
        0.0067973635 = weight(_text_:a in 3366) [ClassicSimilarity], result of:
          0.0067973635 = score(doc=3366,freq=10.0), product of:
            0.04772363 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.041389145 = queryNorm
            0.14243183 = fieldWeight in 3366, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3366)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Documents in rich text corpora usually contain multiple facets of information. For example, an article about a specific disease often consists of different facets such as symptom, treatment, cause, diagnosis, prognosis, and prevention. Thus, documents may have different relations based on different facets. Powerful search tools have been developed to help users locate lists of individual documents that are most related to specific keywords. However, there is a lack of effective analysis tools that reveal the multifaceted relations of documents within or cross the document clusters. In this paper, we present FacetAtlas, a multifaceted visualization technique for visually analyzing rich text corpora. FacetAtlas combines search technology with advanced visual analytical tools to convey both global and local patterns simultaneously. We describe several unique aspects of FacetAtlas, including (1) node cliques and multifaceted edges, (2) an optimized density map, and (3) automated opacity pattern enhancement for highlighting visual patterns, (4) interactive context switch between facets. In addition, we demonstrate the power of FacetAtlas through a case study that targets patient education in the health care domain. Our evaluation shows the benefits of this work, especially in support of complex multifaceted data analysis.

Type

a

0.0010639556 = product of:
  0.0021279112 = sum of:
    0.0021279112 = product of:
      0.0042558224 = sum of:
        0.0042558224 = weight(_text_:a in 3925) [ClassicSimilarity], result of:
          0.0042558224 = score(doc=3925,freq=2.0), product of:
            0.04772363 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.041389145 = queryNorm
            0.089176424 = fieldWeight in 3925, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3925)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a