Search (10 results, page 1 of 1)

0.036961667 = sum of:
  0.020148618 = product of:
    0.08059447 = sum of:
      0.08059447 = weight(_text_:authors in 664) [ClassicSimilarity], result of:
        0.08059447 = score(doc=664,freq=4.0), product of:
          0.22628894 = queryWeight, product of:
            4.558814 = idf(docFreq=1258, maxDocs=44218)
            0.04963768 = queryNorm
          0.35615736 = fieldWeight in 664, 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=664)
    0.25 = coord(1/4)
  0.016813051 = product of:
    0.033626102 = sum of:
      0.033626102 = weight(_text_:22 in 664) [ClassicSimilarity], result of:
        0.033626102 = score(doc=664,freq=2.0), product of:
          0.1738227 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.04963768 = queryNorm
          0.19345059 = fieldWeight in 664, 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=664)
    0.5 = coord(1/2)

Abstract

A new challenge, accessing multiple relevant entities, arises from the availability of linked heterogeneous data. In this article, we address more specifically the problem of accessing relevant entities, such as publications and authors within a bibliographic network, given an information need. We propose a novel algorithm, called BibRank, that estimates a joint relevance of documents and authors within a bibliographic network. This model ranks each type of entity using a score propagation algorithm with respect to the query topic and the structure of the underlying bi-type information entity network. Evidence sources, namely content-based and network-based scores, are both used to estimate the topical similarity between connected entities. For this purpose, authorship relationships are analyzed through a language model-based score on the one hand and on the other hand, non topically related entities of the same type are detected through marginal citations. The article reports the results of experiments using the Bibrank algorithm for an information retrieval task. The CiteSeerX bibliographic data set forms the basis for the topical query automatic generation and evaluation. We show that a statistically significant improvement over closely related ranking models is achieved.

Date

22. 3.2013 19:34:49

0.0148061495 = product of:
  0.029612299 = sum of:
    0.029612299 = product of:
      0.118449196 = sum of:
        0.118449196 = weight(_text_:authors in 4348) [ClassicSimilarity], result of:
          0.118449196 = score(doc=4348,freq=6.0), product of:
            0.22628894 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.04963768 = queryNorm
            0.52344227 = fieldWeight in 4348, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.046875 = fieldNorm(doc=4348)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

Ranking authors is vital for identifying a researcher's impact and standing within a scientific field. There are many different ranking methods (e.g., citations, publications, h-index, PageRank, and weighted PageRank), but most of them are topic-independent. This paper proposes topic-dependent ranks based on the combination of a topic model and a weighted PageRank algorithm. The author-conference-topic (ACT) model was used to extract topic distribution of individual authors. Two ways for combining the ACT model with the PageRank algorithm are proposed: simple combination (I_PR) or using a topic distribution as a weighted vector for PageRank (PR_t). Information retrieval was chosen as the test field and representative authors for different topics at different time phases were identified. Principal component analysis (PCA) was applied to analyze the ranking difference between I_PR and PR_t.

0.013450441 = product of:
  0.026900882 = sum of:
    0.026900882 = product of:
      0.053801764 = sum of:
        0.053801764 = weight(_text_:22 in 1431) [ClassicSimilarity], result of:
          0.053801764 = score(doc=1431,freq=2.0), product of:
            0.1738227 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04963768 = queryNorm
            0.30952093 = fieldWeight in 1431, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=1431)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 8.2014 17:05:18

0.013450441 = product of:
  0.026900882 = sum of:
    0.026900882 = product of:
      0.053801764 = sum of:
        0.053801764 = weight(_text_:22 in 1484) [ClassicSimilarity], result of:
          0.053801764 = score(doc=1484,freq=2.0), product of:
            0.1738227 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04963768 = queryNorm
            0.30952093 = fieldWeight in 1484, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=1484)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

13. 9.2014 14:45:22

0.0123384595 = product of:
  0.024676919 = sum of:
    0.024676919 = product of:
      0.098707676 = sum of:
        0.098707676 = weight(_text_:authors in 3472) [ClassicSimilarity], result of:
          0.098707676 = score(doc=3472,freq=6.0), product of:
            0.22628894 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.04963768 = queryNorm
            0.43620193 = fieldWeight in 3472, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3472)
      0.25 = coord(1/4)
  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.

0.0120891705 = product of:
  0.024178341 = sum of:
    0.024178341 = product of:
      0.096713364 = sum of:
        0.096713364 = weight(_text_:authors in 4349) [ClassicSimilarity], result of:
          0.096713364 = score(doc=4349,freq=4.0), product of:
            0.22628894 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.04963768 = queryNorm
            0.42738882 = fieldWeight in 4349, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.046875 = fieldNorm(doc=4349)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

Ranking scientific productivity and prestige are often limited to homogeneous networks. These networks are unable to account for the multiple factors that constitute the scholarly communication and reward system. This study proposes a new informetric indicator, P-Rank, for measuring prestige in heterogeneous scholarly networks containing articles, authors, and journals. P-Rank differentiates the weight of each citation based on its citing papers, citing journals, and citing authors. Articles from 16 representative library and information science journals are selected as the dataset. Principle Component Analysis is conducted to examine the relationship between P-Rank and other bibliometric indicators. We also compare the correlation and rank variances between citation counts and P-Rank scores. This work provides a new approach to examining prestige in scholarly communication networks in a more comprehensive and nuanced way.

0.0120891705 = product of:
  0.024178341 = sum of:
    0.024178341 = product of:
      0.096713364 = sum of:
        0.096713364 = weight(_text_:authors in 92) [ClassicSimilarity], result of:
          0.096713364 = score(doc=92,freq=4.0), product of:
            0.22628894 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.04963768 = queryNorm
            0.42738882 = fieldWeight in 92, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.046875 = fieldNorm(doc=92)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

In this paper the authors will present research on the combination of two methods of data mining: text classification and maximal association rules. Text classification has been the focus of interest of many researchers for a long time. However, the results take the form of lists of words (classes) that people often do not know what to do with. The use of maximal association rules induced a number of advantages: (1) the detection of dependencies and correlations between the relevant units of information (words) of different classes, (2) the extraction of hidden knowledge, often relevant, from a large volume of data. The authors will show how this combination can improve the process of information retrieval.

0.011888622 = product of:
  0.023777245 = sum of:
    0.023777245 = product of:
      0.04755449 = sum of:
        0.04755449 = weight(_text_:22 in 2591) [ClassicSimilarity], result of:
          0.04755449 = score(doc=2591,freq=4.0), product of:
            0.1738227 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04963768 = queryNorm
            0.27358043 = fieldWeight in 2591, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2591)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

20. 1.2015 18:30:22
18. 9.2018 18:22:56

0.008548334 = product of:
  0.017096668 = sum of:
    0.017096668 = product of:
      0.068386674 = sum of:
        0.068386674 = weight(_text_:authors in 101) [ClassicSimilarity], result of:
          0.068386674 = score(doc=101,freq=2.0), product of:
            0.22628894 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.04963768 = queryNorm
            0.30220953 = fieldWeight in 101, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.046875 = fieldNorm(doc=101)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

Question Answering is an area of information retrieval with the added challenge of applying sophisticated techniques to identify the complex syntactic and semantic relationships present in text in order to provide a more sophisticated and satisfactory response to the user's information needs. For this reason, the authors see question answering as the next step beyond standard information retrieval. In this chapter state of the art question answering is covered focusing on providing an overview of systems, techniques and approaches that are likely to be employed in the next generations of search engines. Special attention is paid to question answering using the World Wide Web as the data source and to question answering exploiting the possibilities of Semantic Web. Considerations about the current issues and prospects for promising future research are also provided.

0.0084065255 = product of:
  0.016813051 = sum of:
    0.016813051 = product of:
      0.033626102 = sum of:
        0.033626102 = weight(_text_:22 in 241) [ClassicSimilarity], result of:
          0.033626102 = score(doc=241,freq=2.0), product of:
            0.1738227 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04963768 = queryNorm
            0.19345059 = fieldWeight in 241, 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=241)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

11. 6.2012 14:22:34