Search (64 results, page 2 of 4)

0.0134057235 = product of:
  0.04021717 = sum of:
    0.04021717 = product of:
      0.08043434 = sum of:
        0.08043434 = weight(_text_:indexing in 7818) [ClassicSimilarity], result of:
          0.08043434 = score(doc=7818,freq=2.0), product of:
            0.19018644 = queryWeight, product of:
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.049684696 = queryNorm
            0.42292362 = fieldWeight in 7818, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.078125 = fieldNorm(doc=7818)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Provides an introduction to the computational techniques that underlie best match searching retrieval systems. Discusses: problems of traditional Boolean systems; characteristics of best-match searching; automatic indexing; term conflation; matching of documents and queries (dealing with similarity measures, initial weights, relevance weights, and the matching algorithm); and describes operational best-match systems

0.0134057235 = product of:
  0.04021717 = sum of:
    0.04021717 = product of:
      0.08043434 = sum of:
        0.08043434 = weight(_text_:indexing in 4114) [ClassicSimilarity], result of:
          0.08043434 = score(doc=4114,freq=2.0), product of:
            0.19018644 = queryWeight, product of:
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.049684696 = queryNorm
            0.42292362 = fieldWeight in 4114, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.078125 = fieldNorm(doc=4114)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

0.013270989 = product of:
  0.039812967 = sum of:
    0.039812967 = product of:
      0.079625934 = sum of:
        0.079625934 = weight(_text_:indexing in 1004) [ClassicSimilarity], result of:
          0.079625934 = score(doc=1004,freq=4.0), product of:
            0.19018644 = queryWeight, product of:
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.049684696 = queryNorm
            0.41867304 = fieldWeight in 1004, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1004)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Most previous information retrieval (IR) models assume that terms of queries and documents are statistically independent from each other. However, conditional independence assumption is obviously and openly understood to be wrong, so we present a new method of incorporating term dependence into a probabilistic retrieval model by adapting a dependency structured indexing system using a dependency parse tree and Chow Expansion to compensate the weakness of the assumption. In this paper, we describe a theoretic process to apply the Chow Expansion to the general probabilistic models and the state-of-the-art 2-Poisson model. Through experiments on document collections in English and Korean, we demonstrate that the incorporation of term dependences using Chow Expansion contributes to the improvement of performance in probabilistic IR systems.

0.011609698 = product of:
  0.03482909 = sum of:
    0.03482909 = product of:
      0.06965818 = sum of:
        0.06965818 = weight(_text_:indexing in 1928) [ClassicSimilarity], result of:
          0.06965818 = score(doc=1928,freq=6.0), product of:
            0.19018644 = queryWeight, product of:
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.049684696 = queryNorm
            0.3662626 = fieldWeight in 1928, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1928)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Reports on a novel technique for literature indexing and searching in a mechanized library system. The notion of relevance is taken as the key concept in the theory of information retrieval and a comparative concept of relevance is explicated in terms of the theory of probability. The resulting technique called 'Probabilistic indexing' allows a computing machine, given a request for information, to make a statistical inference and derive a number (called the 'relevance number') for each document, which is a measure of the probability that the document will satisfy the given request. The result of a search is an ordered list of those documents which satisfy the request ranked according to their probable relevance. The paper goes on to show that whereas in a conventional library system the cross-referencing ('see' and 'see also') is based soley on the 'semantic closeness' between index terms, statistical measures of closeness between index terms can be defined and computed. Thus, given an arbitrary request consisting of one (or many) index term(s), a machine can eleborate on it to increase the probability of selecting relevant documents that would not otherwise have been selected. Finally, the paper suggest an interpretation of the whole library problem as one where the request is considered as a clue on the basis of which the library system makes a concatenated statistical inference in order to provide as an output an ordered list of those documents which most probably satisfy the information needs of the user

0.011609698 = product of:
  0.03482909 = sum of:
    0.03482909 = product of:
      0.06965818 = sum of:
        0.06965818 = weight(_text_:indexing in 3457) [ClassicSimilarity], result of:
          0.06965818 = score(doc=3457,freq=6.0), product of:
            0.19018644 = queryWeight, product of:
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.049684696 = queryNorm
            0.3662626 = fieldWeight in 3457, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3457)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

When people search for documents, they eventually want content, not words. Hence, search engines should relate documents more by their underlying concepts than by the words they contain. One promising technique to do so is Latent Semantic Indexing (LSI). LSI dramatically reduces the dimension of the document space by mapping it into a space spanned by conceptual indices. Empirically, the number of concepts that can represent the documents are far fewer than the great variety of words in the textual representation. Although this almost obviates the problem of lexical matching, the mapping incurs a high computational cost compared to document parsing, indexing, query matching, and updating. This article accomplishes several things. First, it shows how the technique underlying LSI is just one example of a unitary operator, for which there are computationally more attractive alternatives. Second, it proposes the Haar transform as such an alternative, as it is memory efficient, and can be computed in linear to sublinear time. Third, it generalizes LSI by a multiresolution representation of the document space. The approach not only preserves the advantages of LSI at drastically reduced computational costs, it also opens a spectrum of possibilities for new research.

Object

Latent Semantic Indexing

0.011375135 = product of:
  0.034125403 = sum of:
    0.034125403 = product of:
      0.068250805 = sum of:
        0.068250805 = weight(_text_:indexing in 4167) [ClassicSimilarity], result of:
          0.068250805 = score(doc=4167,freq=4.0), product of:
            0.19018644 = queryWeight, product of:
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.049684696 = queryNorm
            0.3588626 = fieldWeight in 4167, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.046875 = fieldNorm(doc=4167)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

This volume contains selected papers that focus on the use of linear algebra, computational statistics, and computer science in the development of algorithms and software systems for text retrieval. Experts in information modeling and retrieval share their perspectives on the design of scalable but precise text retrieval systems, revealing many of the challenges and obstacles that mathematical and statistical models must overcome to be viable for automated text processing. This very useful proceedings is an excellent companion for courses in information retrieval, applied linear algebra, and applied statistics. Computational Information Retrieval provides background material on vector space models for text retrieval that applied mathematicians, statisticians, and computer scientists may not be familiar with. For graduate students in these areas, several research questions in information modeling are exposed. In addition, several case studies concerning the efficacy of the popular Latent Semantic Analysis (or Indexing) approach are provided.

Object

Latent Semantic Indexing

0.011375135 = product of:
  0.034125403 = sum of:
    0.034125403 = product of:
      0.068250805 = sum of:
        0.068250805 = weight(_text_:indexing in 4295) [ClassicSimilarity], result of:
          0.068250805 = score(doc=4295,freq=4.0), product of:
            0.19018644 = queryWeight, product of:
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.049684696 = queryNorm
            0.3588626 = fieldWeight in 4295, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.046875 = fieldNorm(doc=4295)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

The issue of reducing the space overhead when indexing large text databases is becoming more and more important, as the text collection grow in size. Another subject, which is gaining importance as text databases grow and get more heterogeneous and error prone, is that of flexible string matching. One of the best tools to make the search more flexible is to allow a limited number of differences between the words found and those sought. This is called 'approximate text searching'. which is becoming more and more popular. In recent years some indexing schemes with very low space overhead have appeared, some of them dealing with approximate searching. These low overhead indices (whose most notorious exponent is Glimpse) are modified inverted files, where space is saved by making the lists of occurences point to text blocks instead of exact word positions. Despite their existence, little is known about the expected behaviour of these 'block addressing' indices, and even less is known when it comes to cope with approximate search. Our main contribution is an analytical study of the space-time trade-offs for indexed text searching

0.011375135 = product of:
  0.034125403 = sum of:
    0.034125403 = product of:
      0.068250805 = sum of:
        0.068250805 = weight(_text_:indexing in 5704) [ClassicSimilarity], result of:
          0.068250805 = score(doc=5704,freq=4.0), product of:
            0.19018644 = queryWeight, product of:
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.049684696 = queryNorm
            0.3588626 = fieldWeight in 5704, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.046875 = fieldNorm(doc=5704)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Presents a neural network approach to document semantic indexing. Reports results of a study to apply a Hopfield net algorithm to simulate human associative memory for concept exploration in the domain of computer science and engineering. The INSPEC database, consisting of 320.000 abstracts from leading periodical articles was used as the document test bed. Benchmark tests conformed that 3 parameters: maximum number of activated nodes; maximum allowable error; and maximum number of iterations; were useful in positively influencing network convergence behaviour without negatively impacting central processing unit performance. Another series of benchmark tests was performed to determine the effectiveness of various filtering techniques in reducing the negative impact of noisy input terms. Preliminary user tests conformed expectations that the Hopfield net is potentially useful as an associative memory technique to improve document recall and precision by solving discrepancies between indexer vocabularies and end user vocabularies

0.011375135 = product of:
  0.034125403 = sum of:
    0.034125403 = product of:
      0.068250805 = sum of:
        0.068250805 = weight(_text_:indexing in 5777) [ClassicSimilarity], result of:
          0.068250805 = score(doc=5777,freq=4.0), product of:
            0.19018644 = queryWeight, product of:
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.049684696 = queryNorm
            0.3588626 = fieldWeight in 5777, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.046875 = fieldNorm(doc=5777)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

This book discusses many of the key design issues for building search engines and emphazises the important role that applied mathematics can play in improving information retrieval. The authors discuss not only important data structures, algorithms, and software but also user-centered issues such as interfaces, manual indexing, and document preparation. They also present some of the current problems in information retrieval that many not be familiar to applied mathematicians and computer scientists and some of the driving computational methods (SVD, SDD) for automated conceptual indexing

0.01072458 = product of:
  0.032173738 = sum of:
    0.032173738 = product of:
      0.064347476 = sum of:
        0.064347476 = weight(_text_:indexing in 428) [ClassicSimilarity], result of:
          0.064347476 = score(doc=428,freq=2.0), product of:
            0.19018644 = queryWeight, product of:
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.049684696 = queryNorm
            0.3383389 = fieldWeight in 428, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.0625 = fieldNorm(doc=428)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Examines the RANK command on DIALOG using a statistical analysis of articles in DATABASE as an example. The RANK command was used to find authors, company names, and length of articles. Use of the command revealed a number of complexities and revealed some problematic indexing on the part of the database producers. The LEXIS-NEXIS RANK command was also used, but this fulfils a different function to the command of the same name in DIALOG

0.01072458 = product of:
  0.032173738 = sum of:
    0.032173738 = product of:
      0.064347476 = sum of:
        0.064347476 = weight(_text_:indexing in 7) [ClassicSimilarity], result of:
          0.064347476 = score(doc=7,freq=8.0), product of:
            0.19018644 = queryWeight, product of:
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.049684696 = queryNorm
            0.3383389 = fieldWeight in 7, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.03125 = fieldNorm(doc=7)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

The second edition of Understanding Search Engines: Mathematical Modeling and Text Retrieval follows the basic premise of the first edition by discussing many of the key design issues for building search engines and emphasizing the important role that applied mathematics can play in improving information retrieval. The authors discuss important data structures, algorithms, and software as well as user-centered issues such as interfaces, manual indexing, and document preparation. Significant changes bring the text up to date on current information retrieval methods: for example the addition of a new chapter on link-structure algorithms used in search engines such as Google. The chapter on user interface has been rewritten to specifically focus on search engine usability. In addition the authors have added new recommendations for further reading and expanded the bibliography, and have updated and streamlined the index to make it more reader friendly.

Content

Inhalt: Introduction Document File Preparation - Manual Indexing - Information Extraction - Vector Space Modeling - Matrix Decompositions - Query Representations - Ranking and Relevance Feedback - Searching by Link Structure - User Interface - Book Format Document File Preparation Document Purification and Analysis - Text Formatting - Validation - Manual Indexing - Automatic Indexing - Item Normalization - Inverted File Structures - Document File - Dictionary List - Inversion List - Other File Structures Vector Space Models Construction - Term-by-Document Matrices - Simple Query Matching - Design Issues - Term Weighting - Sparse Matrix Storage - Low-Rank Approximations Matrix Decompositions QR Factorization - Singular Value Decomposition - Low-Rank Approximations - Query Matching - Software - Semidiscrete Decomposition - Updating Techniques Query Management Query Binding - Types of Queries - Boolean Queries - Natural Language Queries - Thesaurus Queries - Fuzzy Queries - Term Searches - Probabilistic Queries Ranking and Relevance Feedback Performance Evaluation - Precision - Recall - Average Precision - Genetic Algorithms - Relevance Feedback Searching by Link Structure HITS Method - HITS Implementation - HITS Summary - PageRank Method - PageRank Adjustments - PageRank Implementation - PageRank Summary User Interface Considerations General Guidelines - Search Engine Interfaces - Form Fill-in - Display Considerations - Progress Indication - No Penalties for Error - Results - Test and Retest - Final Considerations Further Reading

0.009479279 = product of:
  0.028437834 = sum of:
    0.028437834 = product of:
      0.05687567 = sum of:
        0.05687567 = weight(_text_:indexing in 4532) [ClassicSimilarity], result of:
          0.05687567 = score(doc=4532,freq=4.0), product of:
            0.19018644 = queryWeight, product of:
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.049684696 = queryNorm
            0.29905218 = fieldWeight in 4532, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4532)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

This technical note describes straightforward techniques for document indexing and retrieval that have been solidly established through extensive testing and are easy to apply. They are useful for many different types of text material, are viable for very large files, and have the advantage that they do not require special skills or training for searching, but are easy for end users. The document and text retrieval methods described here have a sound theoretical basis, are well established by extensive testing, and the ideas involved are now implemented in some commercial retrieval systems. Testing in the last few years has, in particular, shown that the methods presented here work very well with full texts, not only title and abstracts, and with large files of texts containing three quarters of a million documents. These tests, the TREC Tests (see Harman 1993 - 1997; IP&M 1995), have been rigorous comparative evaluations involving many different approaches to information retrieval. These techniques depend an the use of simple terms for indexing both request and document texts; an term weighting exploiting statistical information about term occurrences; an scoring for request-document matching, using these weights, to obtain a ranked search output; and an relevance feedback to modify request weights or term sets in iterative searching. The normal implementation is via an inverted file organisation using a term list with linked document identifiers, plus counting data, and pointers to the actual texts. The user's request can be a word list, phrases, sentences or extended text.

0.009479279 = product of:
  0.028437834 = sum of:
    0.028437834 = product of:
      0.05687567 = sum of:
        0.05687567 = weight(_text_:indexing in 4998) [ClassicSimilarity], result of:
          0.05687567 = score(doc=4998,freq=4.0), product of:
            0.19018644 = queryWeight, product of:
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.049684696 = queryNorm
            0.29905218 = fieldWeight in 4998, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4998)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Documents usually have a content and a structure. The content refers to the text of the document, whereas the structure refers to how a document is logically organized. An increasingly common way to encode the structure is through the use of a mark-up language. Nowadays, the most widely used mark-up language for representing structure is the eXtensible Mark-up Language (XML). XML can be used to provide a focused access to documents, i.e. returning XML elements, such as sections and paragraphs, instead of whole documents in response to a query. Such focused strategies are of particular benefit for information repositories containing long documents, or documents covering a wide variety of topics, where users are directed to the most relevant content within a document. The increased adoption of XML to represent a document structure requires the development of tools to effectively access documents marked-up in XML. This book provides a detailed description of query languages, indexing strategies, ranking algorithms, presentation scenarios developed to access XML documents. Major advances in XML retrieval were seen from 2002 as a result of INEX, the Initiative for Evaluation of XML Retrieval. INEX, also described in this book, provided test sets for evaluating XML retrieval effectiveness. Many of the developments and results described in this book were investigated within INEX.

Content

Table of Contents: Introduction / Basic XML Concepts / Historical Perspectives / Query Languages / Indexing Strategies / Ranking Strategies / Presentation Strategies / Evaluating XML Retrieval Effectiveness / Conclusions

0.009479279 = product of:
  0.028437834 = sum of:
    0.028437834 = product of:
      0.05687567 = sum of:
        0.05687567 = weight(_text_:indexing in 278) [ClassicSimilarity], result of:
          0.05687567 = score(doc=278,freq=4.0), product of:
            0.19018644 = queryWeight, product of:
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.049684696 = queryNorm
            0.29905218 = fieldWeight in 278, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.0390625 = fieldNorm(doc=278)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

State-of-the-art search engine ranking methods combine several distinct sources of relevance evidence to produce a high-quality ranking of results for each query. The fusion of information is currently done at query-processing time, which has a direct effect on the response time of search systems. Previous research also shows that an alternative to improve search efficiency in textual databases is to precompute term impacts at indexing time. In this article, we propose a novel alternative to precompute term impacts, providing a generic framework for combining any distinct set of sources of evidence by using a machine-learning technique. This method retains the advantages of producing high-quality results, but avoids the costs of combining evidence at query-processing time. Our method, called Learn to Precompute Evidence Fusion (LePrEF), uses genetic programming to compute a unified precomputed impact value for each term found in each document prior to query processing, at indexing time. Compared with previous research on precomputing term impacts, our method offers the advantage of providing a generic framework to precompute impact using any set of relevance evidence at any text collection, whereas previous research articles do not. The precomputed impact values are indexed and used later for computing document ranking at query-processing time. By doing so, our method effectively reduces the query processing to simple additions of such impacts. We show that this approach, while leading to results comparable to state-of-the-art ranking methods, also can lead to a significant decrease in computational costs during query processing.

0.009384007 = product of:
  0.02815202 = sum of:
    0.02815202 = product of:
      0.05630404 = sum of:
        0.05630404 = weight(_text_:indexing in 7434) [ClassicSimilarity], result of:
          0.05630404 = score(doc=7434,freq=2.0), product of:
            0.19018644 = queryWeight, product of:
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.049684696 = queryNorm
            0.29604656 = fieldWeight in 7434, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.0546875 = fieldNorm(doc=7434)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Presents a comprehensive approach to automatic organization and hybrid navigation of text databases. An organizing stage builds a particular lattice representation of the data, through text indexing followed by lattice clustering of the indexed texts. The lattice representation supports the navigation state of the system, a visual retrieval interface that combines 3 main retrieval strategies: browsing, querying, and bounding. Such a hybrid paradigm permits high flexibility in trading off information exploration and retrieval, and had good retrieval performance. Compares information retrieval using lattice-based hybrid navigation with conventional Boolean querying. Experiments conducted on 2 medium-sized bibliographic databases showed that the performance of lattice retrieval was comparable to or better than Boolean retrieval

0.009384007 = product of:
  0.02815202 = sum of:
    0.02815202 = product of:
      0.05630404 = sum of:
        0.05630404 = weight(_text_:indexing in 1076) [ClassicSimilarity], result of:
          0.05630404 = score(doc=1076,freq=2.0), product of:
            0.19018644 = queryWeight, product of:
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.049684696 = queryNorm
            0.29604656 = fieldWeight in 1076, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.8278677 = idf(docFreq=2614, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1076)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Reports on a theoretical model of structured document indexing and retrieval based on the Dempster-Schafer Theory of Evidence. Includes a description of the model of structured document retrieval, the representation of structured documents, the representation of individual components, how components are combined, details of the combination process, and how relevance is captured within the model. Also presents a detailed account of an implementation of the model, and an evaluation scheme designed to test the effectiveness of the model

0.008975455 = product of:
  0.026926363 = sum of:
    0.026926363 = product of:
      0.053852726 = sum of:
        0.053852726 = weight(_text_:22 in 5108) [ClassicSimilarity], result of:
          0.053852726 = score(doc=5108,freq=2.0), product of:
            0.17398734 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.049684696 = queryNorm
            0.30952093 = fieldWeight in 5108, 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=5108)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

20. 1.2007 18:30:22

0.008975455 = product of:
  0.026926363 = sum of:
    0.026926363 = product of:
      0.053852726 = sum of:
        0.053852726 = weight(_text_:22 in 3499) [ClassicSimilarity], result of:
          0.053852726 = score(doc=3499,freq=2.0), product of:
            0.17398734 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.049684696 = queryNorm
            0.30952093 = fieldWeight in 3499, 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=3499)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

7. 5.1999 15:22:48

0.008975455 = product of:
  0.026926363 = sum of:
    0.026926363 = product of:
      0.053852726 = sum of:
        0.053852726 = weight(_text_:22 in 1422) [ClassicSimilarity], result of:
          0.053852726 = score(doc=1422,freq=2.0), product of:
            0.17398734 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.049684696 = queryNorm
            0.30952093 = fieldWeight in 1422, 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=1422)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Date

22. 3.2003 19:27:23

0.008975455 = product of:
  0.026926363 = sum of:
    0.026926363 = product of:
      0.053852726 = sum of:
        0.053852726 = weight(_text_:22 in 1431) [ClassicSimilarity], result of:
          0.053852726 = score(doc=1431,freq=2.0), product of:
            0.17398734 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.049684696 = 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)
  0.33333334 = coord(1/3)

Date

22. 8.2014 17:05:18