Search (494 results, page 1 of 25)

0.08095287 = product of:
  0.16190574 = sum of:
    0.16190574 = sum of:
      0.11367553 = weight(_text_:90 in 4309) [ClassicSimilarity], result of:
        0.11367553 = score(doc=4309,freq=2.0), product of:
          0.2733978 = queryWeight, product of:
            5.376119 = idf(docFreq=555, maxDocs=44218)
            0.050854117 = queryNorm
          0.415788 = fieldWeight in 4309, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            5.376119 = idf(docFreq=555, maxDocs=44218)
            0.0546875 = fieldNorm(doc=4309)
      0.048230216 = weight(_text_:22 in 4309) [ClassicSimilarity], result of:
        0.048230216 = score(doc=4309,freq=2.0), product of:
          0.17808245 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.050854117 = queryNorm
          0.2708308 = fieldWeight in 4309, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0546875 = fieldNorm(doc=4309)
  0.5 = coord(1/2)

Date

10. 9.2000 17:38:22

Source

Library resources and technical services. 54(2010) no.2, S.90-114

0.06938817 = product of:
  0.13877635 = sum of:
    0.13877635 = sum of:
      0.09743616 = weight(_text_:90 in 4000) [ClassicSimilarity], result of:
        0.09743616 = score(doc=4000,freq=2.0), product of:
          0.2733978 = queryWeight, product of:
            5.376119 = idf(docFreq=555, maxDocs=44218)
            0.050854117 = queryNorm
          0.3563897 = fieldWeight in 4000, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            5.376119 = idf(docFreq=555, maxDocs=44218)
            0.046875 = fieldNorm(doc=4000)
      0.041340183 = weight(_text_:22 in 4000) [ClassicSimilarity], result of:
        0.041340183 = score(doc=4000,freq=2.0), product of:
          0.17808245 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.050854117 = queryNorm
          0.23214069 = fieldWeight in 4000, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=4000)
  0.5 = coord(1/2)

Abstract

Author research impact was examined based on citer analysis (the number of citers as opposed to the number of citations) for 90 highly cited authors grouped into three broad subject areas. Citer-based outcome measures were also compared with more traditional citation-based measures for levels of association. The authors found that there are significant differences in citer-based outcomes among the three broad subject areas examined and that there is a high degree of correlation between citer and citation-based measures for all measures compared, except for two outcomes calculated for the social sciences. Citer-based measures do produce slightly different rankings of authors based on citer counts when compared to more traditional citation counts. Examples are provided. Citation measures may not adequately address the influence, or reach, of an author because citations usually do not address the origin of the citation beyond self-citations.

Date

28. 9.2010 12:54:22

0.06730819 = product of:
  0.13461637 = sum of:
    0.13461637 = product of:
      0.4038491 = sum of:
        0.4038491 = weight(_text_:3a in 1826) [ClassicSimilarity], result of:
          0.4038491 = score(doc=1826,freq=2.0), product of:
            0.43114176 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.050854117 = queryNorm
            0.93669677 = fieldWeight in 1826, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.078125 = fieldNorm(doc=1826)
      0.33333334 = coord(1/3)
  0.5 = coord(1/2)

Source

http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0CDQQFjAE&url=http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F3131107&ei=HzFWVYvGMsiNsgGTyoFI&usg=AFQjCNE2FHUeR9oQTQlNC4TPedv4Mo3DaQ&sig2=Rlzpr7a3BLZZkqZCXXN_IA&bvm=bv.93564037,d.bGg&cad=rja

0.06495834 = product of:
  0.12991668 = sum of:
    0.12991668 = sum of:
      0.08119681 = weight(_text_:90 in 1962) [ClassicSimilarity], result of:
        0.08119681 = score(doc=1962,freq=2.0), product of:
          0.2733978 = queryWeight, product of:
            5.376119 = idf(docFreq=555, maxDocs=44218)
            0.050854117 = queryNorm
          0.29699144 = fieldWeight in 1962, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            5.376119 = idf(docFreq=555, maxDocs=44218)
            0.0390625 = fieldNorm(doc=1962)
      0.048719876 = weight(_text_:22 in 1962) [ClassicSimilarity], result of:
        0.048719876 = score(doc=1962,freq=4.0), product of:
          0.17808245 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.050854117 = queryNorm
          0.27358043 = fieldWeight in 1962, 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=1962)
  0.5 = coord(1/2)

Abstract

This article reports on the second part of an initiative of the authors on researching classification systems with the conceptual model defined by the Functional Requirements for Subject Authority Data (FRSAD) final report. In an earlier study, the authors explored whether the FRSAD conceptual model could be extended beyond subject authority data to model classification data. The focus of the current study is to determine if classification data modeled using FRSAD can be used to solve real-world discovery problems in multicultural and multilingual contexts. The article discusses the relationships between entities (same type or different types) in the context of classification systems that involve multiple translations and/or multicultural implementations. Results of two case studies are presented in detail: (a) two instances of the Dewey Decimal Classification [DDC] (DDC 22 in English, and the Swedish-English mixed translation of DDC 22), and (b) Chinese Library Classification. The use cases of conceptual models in practice are also discussed.

Source

Cataloging and classification quarterly. 52(2014) no.1, S.90-101

0.05782348 = product of:
  0.11564696 = sum of:
    0.11564696 = sum of:
      0.08119681 = weight(_text_:90 in 660) [ClassicSimilarity], result of:
        0.08119681 = score(doc=660,freq=2.0), product of:
          0.2733978 = queryWeight, product of:
            5.376119 = idf(docFreq=555, maxDocs=44218)
            0.050854117 = queryNorm
          0.29699144 = fieldWeight in 660, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            5.376119 = idf(docFreq=555, maxDocs=44218)
            0.0390625 = fieldNorm(doc=660)
      0.034450155 = weight(_text_:22 in 660) [ClassicSimilarity], result of:
        0.034450155 = score(doc=660,freq=2.0), product of:
          0.17808245 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.050854117 = queryNorm
          0.19345059 = fieldWeight in 660, 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=660)
  0.5 = coord(1/2)

Abstract

Several independent authors reported a high share of uncited publications, which include those produced by top scientists. This share was repeatedly reported to exceed 10% of the total papers produced, without any explanation of this phenomenon and the lack of difference in uncitedness between average and successful researchers. In this report, we analyze the uncitedness among two independent groups of highly visible scientists (mathematicians represented by Fields medalists, and researchers in physiology or medicine represented by Nobel Prize laureates in the respective field). Analysis of both groups led to the identical conclusion: over 90% of the uncited database records of highly visible scientists can be explained by the inclusion of editorial materials progress reports presented at international meetings (meeting abstracts), discussion items (letters to the editor, discussion), personalia (biographic items), and by errors of omission and commission of the Web of Science (WoS) database and of the citing documents. Only a marginal amount of original articles and reviews were found to be uncited (0.9 and 0.3%, respectively), which is in strong contrast with the previously reported data, which never addressed the document types among the uncited records.

Date

22. 3.2013 19:21:46

0.04038491 = product of:
  0.08076982 = sum of:
    0.08076982 = product of:
      0.24230945 = sum of:
        0.24230945 = weight(_text_:3a in 400) [ClassicSimilarity], result of:
          0.24230945 = score(doc=400,freq=2.0), product of:
            0.43114176 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.050854117 = queryNorm
            0.56201804 = fieldWeight in 400, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.046875 = fieldNorm(doc=400)
      0.33333334 = coord(1/3)
  0.5 = coord(1/2)

Content

Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.

0.033654094 = product of:
  0.06730819 = sum of:
    0.06730819 = product of:
      0.20192455 = sum of:
        0.20192455 = weight(_text_:3a in 4997) [ClassicSimilarity], result of:
          0.20192455 = score(doc=4997,freq=2.0), product of:
            0.43114176 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.050854117 = queryNorm
            0.46834838 = fieldWeight in 4997, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4997)
      0.33333334 = coord(1/3)
  0.5 = coord(1/2)

Content

PhD Dissertation at International Doctorate School in Information and Communication Technology. Vgl.: https%3A%2F%2Fcore.ac.uk%2Fdownload%2Fpdf%2F150083013.pdf&usg=AOvVaw2n-qisNagpyT0lli_6QbAQ.

0.028707406 = product of:
  0.05741481 = sum of:
    0.05741481 = product of:
      0.11482962 = sum of:
        0.11482962 = weight(_text_:90 in 5263) [ClassicSimilarity], result of:
          0.11482962 = score(doc=5263,freq=4.0), product of:
            0.2733978 = queryWeight, product of:
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.050854117 = queryNorm
            0.42000932 = fieldWeight in 5263, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5263)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Content

DOI:10.5771/0943-7444-2019-2-90.

Source

Knowledge organization. 46(2019) no.2, S.90-103

0.028707406 = product of:
  0.05741481 = sum of:
    0.05741481 = product of:
      0.11482962 = sum of:
        0.11482962 = weight(_text_:90 in 5327) [ClassicSimilarity], result of:
          0.11482962 = score(doc=5327,freq=4.0), product of:
            0.2733978 = queryWeight, product of:
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.050854117 = queryNorm
            0.42000932 = fieldWeight in 5327, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5327)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In this article we examine automated language-independent authorship verification using text examples in several representative Indo-European languages, in cases when the examined texts belong to an open set of authors, that is, the author is unknown. We showcase the set of developed language-dependent and language-independent features, the model of training examples, consisting of pairs of equal features for known and unknown texts, and the appropriate method of authorship verification. An authorship verification accuracy greater than 90% was accomplished via the application of stylometric methods on four different languages (English, Greek, Spanish, and Dutch, while the verification for Dutch is slightly lower). For the multilingual case, the highest authorship verification accuracy using basic machine-learning methods, over 90%, was achieved by the application of the kNN and SVM-SMO methods, using the feature selection method SVM-RFE. The improvement in authorship verification accuracy in multilingual cases, over 94%, was accomplished via ensemble learning methods, with the MultiboostAB method being a bit more accurate, but Random Forest is generally more appropriate

0.028418882 = product of:
  0.056837764 = sum of:
    0.056837764 = product of:
      0.11367553 = sum of:
        0.11367553 = weight(_text_:90 in 2519) [ClassicSimilarity], result of:
          0.11367553 = score(doc=2519,freq=2.0), product of:
            0.2733978 = queryWeight, product of:
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.050854117 = queryNorm
            0.415788 = fieldWeight in 2519, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2519)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Google on Thursday announced one of the biggest changes to its search engine, a rewriting of its algorithm to handle more complex queries that affects 90 percent of all searches. The change, which represents a new approach to search for Google, required the biggest changes to the company's search algorithm since 2000. Now, Google, the world's most popular search engine, will focus more on trying to understand the meanings of and relationships among things, as opposed to its original strategy of matching keywords.

0.028418882 = product of:
  0.056837764 = sum of:
    0.056837764 = product of:
      0.11367553 = sum of:
        0.11367553 = weight(_text_:90 in 2522) [ClassicSimilarity], result of:
          0.11367553 = score(doc=2522,freq=2.0), product of:
            0.2733978 = queryWeight, product of:
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.050854117 = queryNorm
            0.415788 = fieldWeight in 2522, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2522)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Pünktlich zum 15. Geburtstag der Google Suche verkündete Google gestern auf einer Pressekonferenz in der "Gründungs-Garage", dass das bedeutendste Google Update seit dem Caffeine Update im Jahr 2010 und größte Algorithmus-Update seit 2001 schon seit ca. einem Monat aktiv ist. Das aktuelle Update heißt Hummingbird zu deutsch Kollibri. Es soll ca. 90% aller Suchanfragen betreffen und soll im Vergleich zu Caffeine ein echtes Algorithmus-Update sein. Es soll dabei helfen komplexere Suchanfragen besser zu deuten und noch besser die eigentliche Suchintention bzw. Fragestellung hinter einer Suchanfrage zu erkennen sowie passende Dokumente dazu anzubieten. Auch auf Dokumentenebene soll die eigentliche Intention hinter dem Content besser mit der Suchanfrage gematcht werden.

0.026923273 = product of:
  0.053846546 = sum of:
    0.053846546 = product of:
      0.16153963 = sum of:
        0.16153963 = weight(_text_:3a in 5820) [ClassicSimilarity], result of:
          0.16153963 = score(doc=5820,freq=2.0), product of:
            0.43114176 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.050854117 = queryNorm
            0.3746787 = fieldWeight in 5820, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03125 = fieldNorm(doc=5820)
      0.33333334 = coord(1/3)
  0.5 = coord(1/2)

Content

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.

0.02435904 = product of:
  0.04871808 = sum of:
    0.04871808 = product of:
      0.09743616 = sum of:
        0.09743616 = weight(_text_:90 in 3457) [ClassicSimilarity], result of:
          0.09743616 = score(doc=3457,freq=2.0), product of:
            0.2733978 = queryWeight, product of:
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.050854117 = queryNorm
            0.3563897 = fieldWeight in 3457, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.046875 = fieldNorm(doc=3457)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Arabic has a complex structure, which makes it difficult to apply natural language processing (NLP). Much research on Arabic NLP (ANLP) does exist; however, it is not as mature as that of other languages. Finding Arabic roots is an important step toward conducting effective research on most of ANLP applications. The authors have studied and compared six root-finding algorithms with success rates of over 90%. All algorithms of this study did not use the same testing corpus and/or benchmarking measures. They unified the testing process by implementing their own algorithm descriptions and building a corpus out of 3823 triliteral roots, applying 73 triliteral patterns, and with 18 affixes, producing around 27.6 million words. They tested the algorithms with the generated corpus and have obtained interesting results; they offer to share the corpus freely for benchmarking and ANLP research.

0.02435904 = product of:
  0.04871808 = sum of:
    0.04871808 = product of:
      0.09743616 = sum of:
        0.09743616 = weight(_text_:90 in 1212) [ClassicSimilarity], result of:
          0.09743616 = score(doc=1212,freq=2.0), product of:
            0.2733978 = queryWeight, product of:
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.050854117 = queryNorm
            0.3563897 = fieldWeight in 1212, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.046875 = fieldNorm(doc=1212)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

F1000 is a postpublication peer review service for biological and medical research. F1000 recommends important publications in the biomedical literature, and from this perspective F1000 could be an interesting tool for research evaluation. By linking the complete database of F1000 recommendations to the Web of Science bibliographic database, we are able to make a comprehensive comparison between F1000 recommendations and citations. We find that about 2% of the publications in the biomedical literature receive at least one F1000 recommendation. Recommended publications on average receive 1.30 recommendations, and more than 90% of the recommendations are given within half a year after a publication has appeared. There turns out to be a clear correlation between F1000 recommendations and citations. However, the correlation is relatively weak, at least weaker than the correlation between journal impact and citations. More research is needed to identify the main reasons for differences between recommendations and citations in assessing the impact of publications.

0.02435904 = product of:
  0.04871808 = sum of:
    0.04871808 = product of:
      0.09743616 = sum of:
        0.09743616 = weight(_text_:90 in 1533) [ClassicSimilarity], result of:
          0.09743616 = score(doc=1533,freq=2.0), product of:
            0.2733978 = queryWeight, product of:
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.050854117 = queryNorm
            0.3563897 = fieldWeight in 1533, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.046875 = fieldNorm(doc=1533)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In this study, we discover Russian "centers of excellence" and explore patterns of their collaboration with each other and with foreign partners. Highly cited papers serve as a proxy for "excellence" and coauthored papers as a measure of collaborative efforts. We find that currently research institutes (of the Russian Academy of Sciences as well as others) remain the key players despite recent government initiatives to stimulate university science. The contribution of the commercial sector to high-impact research is negligible. More than 90% of Russian highly cited papers involve international collaboration, and Russian institutions often do not play a dominant role. Partnership with U.S., German, U.K., and French scientists increases markedly the probability of a Russian paper becoming highly cited. Patterns of national ("intranational") collaboration in world-class research differ significantly across different types of organizations; the strongest ties are between three nuclear/particle physics centers. Finally, we draw a coauthorship map to visualize collaboration between Russian centers of excellence.

0.02435904 = product of:
  0.04871808 = sum of:
    0.04871808 = product of:
      0.09743616 = sum of:
        0.09743616 = weight(_text_:90 in 2107) [ClassicSimilarity], result of:
          0.09743616 = score(doc=2107,freq=2.0), product of:
            0.2733978 = queryWeight, product of:
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.050854117 = queryNorm
            0.3563897 = fieldWeight in 2107, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.046875 = fieldNorm(doc=2107)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Source

Knowledge organization. 42(2015) no.2, S.90-101

0.02435904 = product of:
  0.04871808 = sum of:
    0.04871808 = product of:
      0.09743616 = sum of:
        0.09743616 = weight(_text_:90 in 2520) [ClassicSimilarity], result of:
          0.09743616 = score(doc=2520,freq=2.0), product of:
            0.2733978 = queryWeight, product of:
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.050854117 = queryNorm
            0.3563897 = fieldWeight in 2520, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.046875 = fieldNorm(doc=2520)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Google hat mit "Hummingbird" einen neuen Suchalgorithmus entwickelt und bereits eingeführt. Dabei handelt es sich laut Google um eine der größten Veränderungen der Suchmaschine, die rund 90 Prozent aller Suchanfragen betrifft. Im Rahmen einer kleinen Veranstaltung zum 15. Geburtstag der Suchmaschine hat Google in die Garage geladen, in der das Unternehmen gegründet wurde. Dabei enthüllte Google eine der bisher größten Veränderungen an der Suchmaschine: Ohne dass Nutzer etwas davon mitbekamen, hat Google vor rund einem Monat seinen Suchalgorithmus ausgetauscht. Der neue Suchalgorithmus mit Codenamen "Hummingbird" soll es Google ermöglichen, Suchanfragen und Beziehungen zwischen Dingen besser zu verstehen. Das soll die Suchmaschine in die Lage versetzen, komplexere Suchanfragen zu verarbeiten, die von Nutzern immer häufiger gestellt werden - auch, weil immer mehr Nutzer Google auf dem Smartphone per Spracheingabe nutzen. Früher versuchte Google lediglich, die Schlüsselwörter in einer Suchanfrage in Webseiten wiederzufinden. Doch seit geraumer Zeit arbeitet Google daran, die Suchanfragen besser zu verstehen, um bessere Suchergebnisse anzuzeigen.

0.02435904 = product of:
  0.04871808 = sum of:
    0.04871808 = product of:
      0.09743616 = sum of:
        0.09743616 = weight(_text_:90 in 3158) [ClassicSimilarity], result of:
          0.09743616 = score(doc=3158,freq=2.0), product of:
            0.2733978 = queryWeight, product of:
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.050854117 = queryNorm
            0.3563897 = fieldWeight in 3158, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.046875 = fieldNorm(doc=3158)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Systematic reviews are essential for evaluating biomedical treatment options, but the growing size and complexity of the available biomedical literature combined with the rigor of the systematic review method mean that systematic reviews are extremely difficult and labor-intensive to perform. In this article, I propose a method of searching the literature by systematically mining the various types of citation relationships between articles. I then test the method by comparing its precision and recall to that of 14 published systematic reviews. The method successfully retrieved 74% of the studies included in these reviews and 90% of the studies it could reasonably be expected to retrieve. The method also retrieved fewer than half of the total number of publications retrieved by these reviews and can be performed in substantially less time. This suggests that the proposed method offers a promising complement to traditional text-based methods of literature identification and retrieval for systematic reviews.

0.02435904 = product of:
  0.04871808 = sum of:
    0.04871808 = product of:
      0.09743616 = sum of:
        0.09743616 = weight(_text_:90 in 3185) [ClassicSimilarity], result of:
          0.09743616 = score(doc=3185,freq=2.0), product of:
            0.2733978 = queryWeight, product of:
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.050854117 = queryNorm
            0.3563897 = fieldWeight in 3185, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.376119 = idf(docFreq=555, maxDocs=44218)
              0.046875 = fieldNorm(doc=3185)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Query suggestion is generally an integrated part of web search engines. In this study, we first redefine and reduce the query suggestion problem as "comparison of queries". We then propose a general modular framework for query suggestion algorithm development. We also develop new query suggestion algorithms which are used in our proposed framework, exploiting query, session and user features. As a case study, we use query logs of a real educational search engine that targets K-12 students in Turkey. We also exploit educational features (course, grade) in our query suggestion algorithms. We test our framework and algorithms over a set of queries by an experiment and demonstrate a 66-90% statistically significant increase in relevance of query suggestions compared to a baseline method.

0.024115108 = product of:
  0.048230216 = sum of:
    0.048230216 = product of:
      0.09646043 = sum of:
        0.09646043 = weight(_text_:22 in 4347) [ClassicSimilarity], result of:
          0.09646043 = score(doc=4347,freq=2.0), product of:
            0.17808245 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050854117 = queryNorm
            0.5416616 = fieldWeight in 4347, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.109375 = fieldNorm(doc=4347)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 3.2013 16:18:36