Search (152 results, page 8 of 8)

0.002798144 = product of:
  0.019587006 = sum of:
    0.019587006 = weight(_text_:based in 1022) [ClassicSimilarity], result of:
      0.019587006 = score(doc=1022,freq=2.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.16644597 = fieldWeight in 1022, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1022)
  0.14285715 = coord(1/7)

Abstract

Bibliometric-enhanced information retrieval uses bibliometrics (e.g., citations) to improve ranking algorithms. Using a data-driven approach, this article describes the development of a bibliometric-enhanced ranking algorithm for legal information retrieval, and the evaluation thereof. We statistically analyze the correlation between usage of documents and citations over time, using data from a commercial legal search engine. We then propose a bibliometric boost function that combines usage of documents with citation counts. The core of this function is an impact variable based on usage and citations that increases in influence as citations and usage counts become more reliable over time. We evaluate our ranking function by comparing search sessions before and after the introduction of the new ranking in the search engine. Using a cost model applied to 129,571 sessions before and 143,864 sessions after the intervention, we show that our bibliometric-enhanced ranking algorithm reduces the time of a search session of legal professionals by 2 to 3% on average for use cases other than known-item retrieval or updating behavior. Given the high hourly tariff of legal professionals and the limited time they can spend on research, this is expected to lead to increased efficiency, especially for users with extremely long search sessions.

0.002645828 = product of:
  0.018520795 = sum of:
    0.018520795 = product of:
      0.03704159 = sum of:
        0.03704159 = weight(_text_:22 in 3276) [ClassicSimilarity], result of:
          0.03704159 = score(doc=3276,freq=2.0), product of:
            0.13677022 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03905679 = queryNorm
            0.2708308 = fieldWeight in 3276, 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=3276)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Date

20. 3.2005 16:23:22

0.0022678524 = product of:
  0.015874967 = sum of:
    0.015874967 = product of:
      0.031749934 = sum of:
        0.031749934 = weight(_text_:22 in 5123) [ClassicSimilarity], result of:
          0.031749934 = score(doc=5123,freq=2.0), product of:
            0.13677022 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03905679 = queryNorm
            0.23214069 = fieldWeight in 5123, 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=5123)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Date

12. 9.1996 13:56:22

0.0022678524 = product of:
  0.015874967 = sum of:
    0.015874967 = product of:
      0.031749934 = sum of:
        0.031749934 = weight(_text_:22 in 6973) [ClassicSimilarity], result of:
          0.031749934 = score(doc=6973,freq=2.0), product of:
            0.13677022 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03905679 = queryNorm
            0.23214069 = fieldWeight in 6973, 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=6973)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Source

Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon

0.0022678524 = product of:
  0.015874967 = sum of:
    0.015874967 = product of:
      0.031749934 = sum of:
        0.031749934 = weight(_text_:22 in 1451) [ClassicSimilarity], result of:
          0.031749934 = score(doc=1451,freq=2.0), product of:
            0.13677022 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03905679 = queryNorm
            0.23214069 = fieldWeight in 1451, 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=1451)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Date

22. 3.2003 19:27:36

0.0022678524 = product of:
  0.015874967 = sum of:
    0.015874967 = product of:
      0.031749934 = sum of:
        0.031749934 = weight(_text_:22 in 2419) [ClassicSimilarity], result of:
          0.031749934 = score(doc=2419,freq=2.0), product of:
            0.13677022 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03905679 = queryNorm
            0.23214069 = fieldWeight in 2419, 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=2419)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Date

16.11.2008 16:22:48

0.002238515 = product of:
  0.015669605 = sum of:
    0.015669605 = weight(_text_:based in 5701) [ClassicSimilarity], result of:
      0.015669605 = score(doc=5701,freq=2.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.13315678 = fieldWeight in 5701, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03125 = fieldNorm(doc=5701)
  0.14285715 = coord(1/7)

0.002238515 = product of:
  0.015669605 = sum of:
    0.015669605 = weight(_text_:based in 801) [ClassicSimilarity], result of:
      0.015669605 = score(doc=801,freq=2.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.13315678 = fieldWeight in 801, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03125 = fieldNorm(doc=801)
  0.14285715 = coord(1/7)

Content

The goal of information retrieval is to find all documents relevant for a user query in a collection of documents. Decades of research in information retrieval were successful in developing and refining techniques that are solely word-based (see e.g., [2]). With the advent of the web new sources of information became available, one of them being the hyperlinks between documents and records of user behavior. To be precise, hypertexts (i.e., collections of documents connected by hyperlinks) have existed and have been studied for a long time. What was new was the large number of hyperlinks created by independent individuals. Hyperlinks provide a valuable source of information for web information retrieval as we will show in this article. This area of information retrieval is commonly called link analysis. Why would one expect hyperlinks to be useful? Ahyperlink is a reference of a web page B that is contained in a web page A. When the hyperlink is clicked on in a web browser, the browser displays page B. This functionality alone is not helpful for web information retrieval. However, the way hyperlinks are typically used by authors of web pages can give them valuable information content. Typically, authors create links because they think they will be useful for the readers of the pages. Thus, links are usually either navigational aids that, for example, bring the reader back to the homepage of the site, or links that point to pages whose content augments the content of the current page. The second kind of links tend to point to high-quality pages that might be on the same topic as the page containing the link.

0.002238515 = product of:
  0.015669605 = sum of:
    0.015669605 = weight(_text_:based in 8) [ClassicSimilarity], result of:
      0.015669605 = score(doc=8,freq=2.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.13315678 = fieldWeight in 8, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03125 = fieldNorm(doc=8)
  0.14285715 = coord(1/7)

Content

Information retrieval is a computer science subfield whose goal is to find all documents relevant to a user query in a given collection of documents. As such, information retrieval should really be called document retrieval. Before the advent of the Web, IR systems were typically installed in libraries for use mostly by reference librarians. The retrieval algorithm for these systems was usually based exclusively on analysis of the words in the document. The Web changed all this. Now each Web user has access to various search engines whose retrieval algorithms often use not only the words in the documents but also information like the hyperlink structure of the Web or markup language tags. How are hyperlinks useful? The hyperlink functionality alone-that is, the hyperlink to Web page B that is contained in Web page A-is not directly useful in information retrieval. However, the way Web page authors use hyperlinks can give them valuable information content. Authors usually create hyperlinks they think will be useful to readers. Some may be navigational aids that, for example, take the reader back to the site's home page; others provide access to documents that augment the content of the current page. The latter tend to point to highquality pages that might be on the same topic as the page containing the hyperlink. Web information retrieval systems can exploit this information to refine searches for relevant documents. Hyperlink analysis significantly improves the relevance of the search results, so much so that all major Web search engines claim to use some type of hyperlink analysis. However, the search engines do not disclose details about the type of hyperlink analysis they perform- mostly to avoid manipulation of search results by Web-positioning companies. In this article, I discuss how hyperlink analysis can be applied to ranking algorithms, and survey other ways Web search engines can use this analysis.

0.002238515 = product of:
  0.015669605 = sum of:
    0.015669605 = weight(_text_:based in 2122) [ClassicSimilarity], result of:
      0.015669605 = score(doc=2122,freq=2.0), product of:
        0.11767787 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03905679 = queryNorm
        0.13315678 = fieldWeight in 2122, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.03125 = fieldNorm(doc=2122)
  0.14285715 = coord(1/7)

Abstract

Intuitively, any 'bag of words' approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies have been mixed or inconclusive. To improve the situation, this paper shows how the natural language properties of the target documents can be used to transform and enrich the term dependencies to more useful statistics. This is done in three steps. The term co-occurrence statistics of queries and documents are each represented by a Markov chain. The paper proves that such a chain is ergodic, and therefore its asymptotic behavior is unique, stationary, and independent of the initial state. Next, the stationary distribution is taken to model queries and documents, rather than their initial distributions. Finally, ranking is achieved following the customary language modeling paradigm. The main contribution of this paper is to argue why the asymptotic behavior of the document model is a better representation then just the document's initial distribution. A secondary contribution is to investigate the practical application of this representation in case the queries become increasingly verbose. In the experiments (based on Lemur's search engine substrate) the default query model was replaced by the stable distribution of the query. Just modeling the query this way already resulted in significant improvements over a standard language model baseline. The results were on a par or better than more sophisticated algorithms that use fine-tuned parameters or extensive training. Moreover, the more verbose the query, the more effective the approach seems to become.

0.0018898771 = product of:
  0.013229139 = sum of:
    0.013229139 = product of:
      0.026458278 = sum of:
        0.026458278 = weight(_text_:22 in 5697) [ClassicSimilarity], result of:
          0.026458278 = score(doc=5697,freq=2.0), product of:
            0.13677022 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03905679 = queryNorm
            0.19345059 = fieldWeight in 5697, 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=5697)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Date

22. 2.1996 13:14:10

0.0018898771 = product of:
  0.013229139 = sum of:
    0.013229139 = product of:
      0.026458278 = sum of:
        0.026458278 = weight(_text_:22 in 56) [ClassicSimilarity], result of:
          0.026458278 = score(doc=56,freq=2.0), product of:
            0.13677022 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03905679 = queryNorm
            0.19345059 = fieldWeight in 56, 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=56)
      0.5 = coord(1/2)
  0.14285715 = coord(1/7)

Date

22. 7.2006 16:32:43