Search (27 results, page 1 of 2)

0.057399243 = product of:
  0.114798486 = sum of:
    0.072267525 = weight(_text_:engines in 1178) [ClassicSimilarity], result of:
      0.072267525 = score(doc=1178,freq=4.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.31755137 = fieldWeight in 1178, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.03125 = fieldNorm(doc=1178)
    0.042530965 = product of:
      0.08506193 = sum of:
        0.08506193 = weight(_text_:programming in 1178) [ClassicSimilarity], result of:
          0.08506193 = score(doc=1178,freq=2.0), product of:
            0.29361802 = queryWeight, product of:
              6.5552235 = idf(docFreq=170, maxDocs=44218)
              0.04479146 = queryNorm
            0.28970268 = fieldWeight in 1178, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              6.5552235 = idf(docFreq=170, maxDocs=44218)
              0.03125 = fieldNorm(doc=1178)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

In Jorge Luis Borges's curious short story The Library of Babel, the narrator describes an endless collection of books stored from floor to ceiling in a labyrinth of countless hexagonal rooms. The pages of the library's books seem to contain random sequences of letters and spaces; occasionally a few intelligible words emerge in the sea of paper and ink. Nevertheless, readers diligently, and exasperatingly, scan the shelves for coherent passages. The narrator himself has wandered numerous rooms in search of enlightenment, but with resignation he simply awaits his death and burial - which Borges explains (with signature dark humor) consists of being tossed unceremoniously over the library's banister. Borges's nightmare, of course, is a cursed vision of the research methods of disciplines such as literature, history, and philosophy, where the careful reading of books, one after the other, is supposed to lead inexorably to knowledge and understanding. Computer scientists would approach Borges's library far differently. Employing the information theory that forms the basis for search engines and other computerized techniques for assessing in one fell swoop large masses of documents, they would quickly realize the collection's incoherence though sampling and statistical methods - and wisely start looking for the library's exit. These computational methods, which allow us to find patterns, determine relationships, categorize documents, and extract information from massive corpuses, will form the basis for new tools for research in the humanities and other disciplines in the coming decade. For the past three years I have been experimenting with how to provide such end-user tools - that is, tools that harness the power of vast electronic collections while hiding much of their complicated technical plumbing. In particular, I have made extensive use of the application programming interfaces (APIs) the leading search engines provide for programmers to query their databases directly (from server to server without using their web interfaces). In addition, I have explored how one might extract information from large digital collections, from the well-curated lexicographic database WordNet to the democratic (and poorly curated) online reference work Wikipedia. While processing these digital corpuses is currently an imperfect science, even now useful tools can be created by combining various collections and methods for searching and analyzing them. And more importantly, these nascent services suggest a future in which information can be gleaned from, and sense can be made out of, even imperfect digital libraries of enormous scale. A brief examination of two approaches to data mining large digital collections hints at this future, while also providing some lessons about how to get there.

0.039523818 = product of:
  0.079047635 = sum of:
    0.06387607 = weight(_text_:engines in 1605) [ClassicSimilarity], result of:
      0.06387607 = score(doc=1605,freq=2.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.2806784 = fieldWeight in 1605, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1605)
    0.015171562 = product of:
      0.030343125 = sum of:
        0.030343125 = weight(_text_:22 in 1605) [ClassicSimilarity], result of:
          0.030343125 = score(doc=1605,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.19345059 = fieldWeight in 1605, 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=1605)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Numerous studies have explored the possibility of uncovering information from web search queries but few have examined the factors that affect web query data sources. We conducted a study that investigated this issue by comparing Google Trends and Baidu Index. Data from these two services are based on queries entered by users into Google and Baidu, two of the largest search engines in the world. We first compared the features and functions of the two services based on documents and extensive testing. We then carried out an empirical study that collected query volume data from the two sources. We found that data from both sources could be used to predict the quality of Chinese universities and companies. Despite the differences between the two services in terms of technology, such as differing methods of language processing, the search volume data from the two were highly correlated and combining the two data sources did not improve the predictive power of the data. However, there was a major difference between the two in terms of data availability. Baidu Index was able to provide more search volume data than Google Trends did. Our analysis showed that the disadvantage of Google Trends in this regard was due to Google's smaller user base in China. The implication of this finding goes beyond China. Google's user bases in many countries are smaller than that in China, so the search volume data related to those countries could result in the same issue as that related to China.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22

0.027659154 = product of:
  0.110636614 = sum of:
    0.110636614 = weight(_text_:engines in 597) [ClassicSimilarity], result of:
      0.110636614 = score(doc=597,freq=6.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.4861493 = fieldWeight in 597, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0390625 = fieldNorm(doc=597)
  0.25 = coord(1/4)

Abstract

With the overwhelming volume of information, the task of finding relevant information on a given topic on the Web is becoming increasingly difficult. Web search engines hence become one of the most popular solutions available on the Web. However, it has never been easy for novice users to organize and represent their information needs using simple queries. Users have to keep modifying their input queries until they get expected results. Therefore, it is often desirable for search engines to give suggestions on related queries to users. Besides, by identifying those related queries, search engines can potentially perform optimizations on their systems, such as query expansion and file indexing. In this work we propose a method that suggests a list of related queries given an initial input query. The related queries are based in the query log of previously submitted queries by human users, which can be identified using an enhanced model of association rules. Users can utilize the suggested related queries to tune or redirect the search process. Our method not only discovers the related queries, but also ranks them according to the degree of their relatedness. Unlike many other rival techniques, it also performs reasonably well on less frequent input queries.

0.027624674 = product of:
  0.1104987 = sum of:
    0.1104987 = product of:
      0.2209974 = sum of:
        0.2209974 = weight(_text_:programming in 4863) [ClassicSimilarity], result of:
          0.2209974 = score(doc=4863,freq=6.0), product of:
            0.29361802 = queryWeight, product of:
              6.5552235 = idf(docFreq=170, maxDocs=44218)
              0.04479146 = queryNorm
            0.7526697 = fieldWeight in 4863, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              6.5552235 = idf(docFreq=170, maxDocs=44218)
              0.046875 = fieldNorm(doc=4863)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

In data mining, we emphasize the need for learning from huge, incomplete, and imperfect data sets. To handle noise in the problem domain, existing learning systems avoid overfitting the imperfect training examples by excluding insignificant patterns. The problem is that these systems use a limiting attribute-value language for representing the training examples and the induced knowledge. Moreover, some important patterns are ignored because they are statistically insignificant. In this article, we present a framework that combines genetic programming and inductive logic programming to induce knowledge represented in various knowledge representation formalisms from noisy databases (LOGENPRO). Moreover, the system is applied to one real-life medical database. The knowledge discovered provides insights to and allows better understanding of the medical domains

0.027100323 = product of:
  0.10840129 = sum of:
    0.10840129 = weight(_text_:engines in 91) [ClassicSimilarity], result of:
      0.10840129 = score(doc=91,freq=4.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.47632706 = fieldWeight in 91, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.046875 = fieldNorm(doc=91)
  0.25 = coord(1/4)

Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64425.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

0.027100323 = product of:
  0.10840129 = sum of:
    0.10840129 = weight(_text_:engines in 92) [ClassicSimilarity], result of:
      0.10840129 = score(doc=92,freq=4.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.47632706 = fieldWeight in 92, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.046875 = fieldNorm(doc=92)
  0.25 = coord(1/4)

Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64430.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

0.022583602 = product of:
  0.09033441 = sum of:
    0.09033441 = weight(_text_:engines in 604) [ClassicSimilarity], result of:
      0.09033441 = score(doc=604,freq=4.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.39693922 = fieldWeight in 604, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0390625 = fieldNorm(doc=604)
  0.25 = coord(1/4)

Abstract

Modern information retrieval systems use keywords within documents as indexing terms for search of relevant documents. As Chinese is an ideographic character-based language, the words in the texts are not delimited by white spaces. Indexing of Chinese documents is impossible without a proper segmentation algorithm. Many Chinese segmentation algorithms have been proposed in the past. Traditional segmentation algorithms cannot operate without a large dictionary or a large corpus of training data. Nowadays, the Web has become the largest corpus that is ideal for Chinese segmentation. Although most search engines have problems in segmenting texts into proper words, they maintain huge databases of documents and frequencies of character sequences in the documents. Their databases are important potential resources for segmentation. In this paper, we propose a segmentation algorithm by mining Web data with the help of search engines. On the other hand, the Romanized pinyin of Chinese language indicates boundaries of words in the text. Our algorithm is the first to utilize the Romanized pinyin to segmentation. It is the first unified segmentation algorithm for the Chinese language from different geographical areas, and it is also domain independent because of the nature of the Web. Experiments have been conducted on the datasets of a recent Chinese segmentation competition. The results show that our algorithm outperforms the traditional algorithms in terms of precision and recall. Moreover, our algorithm can effectively deal with the problems of segmentation ambiguity, new word (unknown word) detection, and stop words.

0.022583602 = product of:
  0.09033441 = sum of:
    0.09033441 = weight(_text_:engines in 1046) [ClassicSimilarity], result of:
      0.09033441 = score(doc=1046,freq=4.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.39693922 = fieldWeight in 1046, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1046)
  0.25 = coord(1/4)

Abstract

The dynamic nature and size of the Internet can result in difficulty finding relevant information. Most users typically express their information need via short queries to search engines and they often have to physically sift through the search results based on relevance ranking set by the search engines, making the process of relevance judgement time-consuming. In this paper, we describe a novel representation technique which makes use of the Web structure together with summarisation techniques to better represent knowledge in actual Web Documents. We named the proposed technique as Semantic Virtual Document (SVD). We will discuss how the proposed SVD can be used together with a suitable clustering algorithm to achieve an automatic content-based categorization of similar Web Documents. The auto-categorization facility as well as a "Tree-like" Graphical User Interface (GUI) for post-retrieval document browsing enhances the relevance judgement process for Internet users. Furthermore, we will introduce how our cluster-biased automatic query expansion technique can be used to overcome the ambiguity of short queries typically given by users. We will outline our experimental design to evaluate the effectiveness of the proposed SVD for representation and present a prototype called iSEARCH (Intelligent SEarch And Review of Cluster Hierarchy) for Web content mining. Our results confirm, quantify and extend previous research using Web structure and summarisation techniques, introducing novel techniques for knowledge representation to enhance Web content mining.

0.022356624 = product of:
  0.089426495 = sum of:
    0.089426495 = weight(_text_:engines in 601) [ClassicSimilarity], result of:
      0.089426495 = score(doc=601,freq=2.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.39294976 = fieldWeight in 601, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0546875 = fieldNorm(doc=601)
  0.25 = coord(1/4)

0.022356624 = product of:
  0.089426495 = sum of:
    0.089426495 = weight(_text_:engines in 4676) [ClassicSimilarity], result of:
      0.089426495 = score(doc=4676,freq=2.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.39294976 = fieldWeight in 4676, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4676)
  0.25 = coord(1/4)

Abstract

This paper discusses an approach of collecting qualitative data on human information behaviour that is based on mining web data using search engines. The approach is technically the same that has been used for some time in webometric research to make statistical inferences on web data, but the present paper shows how the same tools and data collecting methods can be used to gather data for qualitative data analysis on human information behaviour.

0.01916282 = product of:
  0.07665128 = sum of:
    0.07665128 = weight(_text_:engines in 1326) [ClassicSimilarity], result of:
      0.07665128 = score(doc=1326,freq=2.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.33681408 = fieldWeight in 1326, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.046875 = fieldNorm(doc=1326)
  0.25 = coord(1/4)

Abstract

The effective use of corporate memory is becoming increasingly important because every aspect of e-business requires access to information repositories. Unfortunately, less-than-satisfying effectiveness in state-of-the-art information-retrieval techniques is well known, even for some of the best search engines such as Google. In this study, the authors resolve this retrieval ineffectiveness problem by developing a new framework for predicting query performance, which is the first step toward better retrieval effectiveness. Specifically, they examine the relationship between query performance and query context. A query context consists of the query itself, the document collection, and the interaction between the two. The authors first analyze the characteristics of query context and develop various features for predicting query performance. Then, they propose a context-sensitive model for predicting query performance based on the characteristics of the query and the document collection. Finally, they validate this model with respect to five real-world collections of documents and demonstrate its utility in routing queries to the correct repository with high accuracy.

0.015969018 = product of:
  0.06387607 = sum of:
    0.06387607 = weight(_text_:engines in 607) [ClassicSimilarity], result of:
      0.06387607 = score(doc=607,freq=2.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.2806784 = fieldWeight in 607, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0390625 = fieldNorm(doc=607)
  0.25 = coord(1/4)

Abstract

Understanding what kinds of Web pages are the most useful for Web search engine users is a critical task in Web information retrieval (IR). Most previous works used hyperlink analysis algorithms to solve this problem. However, little research has been focused on query-independent Web data cleansing for Web IR. In this paper, we first provide analysis of the differences between retrieval target pages and ordinary ones based on more than 30 million Web pages obtained from both the Text Retrieval Conference (TREC) and a widely used Chinese search engine, SOGOU (www.sogou.com). We further propose a learning-based data cleansing algorithm for reducing Web pages that are unlikely to be useful for user requests. We found that there exists a large proportion of low-quality Web pages in both the English and the Chinese Web page corpus, and retrieval target pages can be identified using query-independent features and cleansing algorithms. The experimental results showed that our algorithm is effective in reducing a large portion of Web pages with a small loss in retrieval target pages. It makes it possible for Web IR tools to meet a large fraction of users' needs with only a small part of pages on the Web. These results may help Web search engines make better use of their limited storage and computation resources to improve search performance.

0.010620093 = product of:
  0.042480372 = sum of:
    0.042480372 = product of:
      0.084960744 = sum of:
        0.084960744 = weight(_text_:22 in 4577) [ClassicSimilarity], result of:
          0.084960744 = score(doc=4577,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.5416616 = fieldWeight in 4577, 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=4577)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

2. 4.2000 18:01:22

0.009102937 = product of:
  0.036411747 = sum of:
    0.036411747 = product of:
      0.072823495 = sum of:
        0.072823495 = weight(_text_:22 in 6783) [ClassicSimilarity], result of:
          0.072823495 = score(doc=6783,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.46428138 = fieldWeight in 6783, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.09375 = fieldNorm(doc=6783)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Footnote

A special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997

0.0060686246 = product of:
  0.024274498 = sum of:
    0.024274498 = product of:
      0.048548996 = sum of:
        0.048548996 = weight(_text_:22 in 1737) [ClassicSimilarity], result of:
          0.048548996 = score(doc=1737,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.30952093 = fieldWeight in 1737, 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=1737)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

22.11.1998 18:57:22

0.0060686246 = product of:
  0.024274498 = sum of:
    0.024274498 = product of:
      0.048548996 = sum of:
        0.048548996 = weight(_text_:22 in 4261) [ClassicSimilarity], result of:
          0.048548996 = score(doc=4261,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.30952093 = fieldWeight in 4261, 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=4261)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

17. 7.2002 19:22:06

0.0060686246 = product of:
  0.024274498 = sum of:
    0.024274498 = product of:
      0.048548996 = sum of:
        0.048548996 = weight(_text_:22 in 1270) [ClassicSimilarity], result of:
          0.048548996 = score(doc=1270,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.30952093 = fieldWeight in 1270, 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=1270)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Source

Information systems. 22(1997) nos.5/6, S.333-347

0.0053100465 = product of:
  0.021240186 = sum of:
    0.021240186 = product of:
      0.042480372 = sum of:
        0.042480372 = weight(_text_:22 in 2908) [ClassicSimilarity], result of:
          0.042480372 = score(doc=2908,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.2708308 = fieldWeight in 2908, 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=2908)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Source

Information systems. 22(1997) nos.5/6, S.349-385

0.0045514684 = product of:
  0.018205874 = sum of:
    0.018205874 = product of:
      0.036411747 = sum of:
        0.036411747 = weight(_text_:22 in 1383) [ClassicSimilarity], result of:
          0.036411747 = score(doc=1383,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.23214069 = fieldWeight in 1383, 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=1383)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

22. 3.2008 14:46:06

0.0037928906 = product of:
  0.015171562 = sum of:
    0.015171562 = product of:
      0.030343125 = sum of:
        0.030343125 = weight(_text_:22 in 668) [ClassicSimilarity], result of:
          0.030343125 = score(doc=668,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.19345059 = fieldWeight in 668, 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=668)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

22. 3.2013 19:43:01