-
Chowdhury, G.G.: Template mining for information extraction from digital documents (1999)
0.03
0.029650759 = product of:
0.059301518 = sum of:
0.059301518 = product of:
0.118603036 = sum of:
0.118603036 = weight(_text_:22 in 4577) [ClassicSimilarity], result of:
0.118603036 = score(doc=4577,freq=2.0), product of:
0.21896149 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.06252774 = 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)
0.5 = coord(1/2)
- Date
- 2. 4.2000 18:01:22
-
Maaten, L. van den; Hinton, G.: Visualizing non-metric similarities in multiple maps (2012)
0.03
0.027600117 = product of:
0.055200234 = sum of:
0.055200234 = product of:
0.1656007 = sum of:
0.1656007 = weight(_text_:objects in 3884) [ClassicSimilarity], result of:
0.1656007 = score(doc=3884,freq=4.0), product of:
0.33233938 = queryWeight, product of:
5.315071 = idf(docFreq=590, maxDocs=44218)
0.06252774 = queryNorm
0.49828792 = fieldWeight in 3884, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
5.315071 = idf(docFreq=590, maxDocs=44218)
0.046875 = fieldNorm(doc=3884)
0.33333334 = coord(1/3)
0.5 = coord(1/2)
- Abstract
- Techniques for multidimensional scaling visualize objects as points in a low-dimensional metric map. As a result, the visualizations are subject to the fundamental limitations of metric spaces. These limitations prevent multidimensional scaling from faithfully representing non-metric similarity data such as word associations or event co-occurrences. In particular, multidimensional scaling cannot faithfully represent intransitive pairwise similarities in a visualization, and it cannot faithfully visualize "central" objects. In this paper, we present an extension of a recently proposed multidimensional scaling technique called t-SNE. The extension aims to address the problems of traditional multidimensional scaling techniques when these techniques are used to visualize non-metric similarities. The new technique, called multiple maps t-SNE, alleviates these problems by constructing a collection of maps that reveal complementary structure in the similarity data. We apply multiple maps t-SNE to a large data set of word association data and to a data set of NIPS co-authorships, demonstrating its ability to successfully visualize non-metric similarities.
-
Fayyad, U.M.; Djorgovski, S.G.; Weir, N.: From digitized images to online catalogs : data ming a sky server (1996)
0.03
0.026021637 = product of:
0.052043274 = sum of:
0.052043274 = product of:
0.15612982 = sum of:
0.15612982 = weight(_text_:objects in 6625) [ClassicSimilarity], result of:
0.15612982 = score(doc=6625,freq=2.0), product of:
0.33233938 = queryWeight, product of:
5.315071 = idf(docFreq=590, maxDocs=44218)
0.06252774 = queryNorm
0.46979034 = fieldWeight in 6625, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.315071 = idf(docFreq=590, maxDocs=44218)
0.0625 = fieldNorm(doc=6625)
0.33333334 = coord(1/3)
0.5 = coord(1/2)
- Abstract
- Offers a data mining approach based on machine learning classification methods to the problem of automated cataloguing of online databases of digital images resulting from sky surveys. The SKICAT system automates the reduction and analysis of 3 terabytes of images expected to contain about 2 billion sky objects. It offers a solution to problems associated with the analysis of large data sets in science
-
KDD : techniques and applications (1998)
0.03
0.025414936 = product of:
0.050829872 = sum of:
0.050829872 = product of:
0.101659745 = sum of:
0.101659745 = weight(_text_:22 in 6783) [ClassicSimilarity], result of:
0.101659745 = score(doc=6783,freq=2.0), product of:
0.21896149 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.06252774 = 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)
0.5 = coord(1/2)
- 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
-
Datentracking in der Wissenschaft : Aggregation und Verwendung bzw. Verkauf von Nutzungsdaten durch Wissenschaftsverlage. Ein Informationspapier des Ausschusses für Wissenschaftliche Bibliotheken und Informationssysteme der Deutschen Forschungsgemeinschaft (2021)
0.02
0.023109794 = product of:
0.046219587 = sum of:
0.046219587 = product of:
0.092439175 = sum of:
0.092439175 = weight(_text_:deutsche in 248) [ClassicSimilarity], result of:
0.092439175 = score(doc=248,freq=2.0), product of:
0.2952815 = queryWeight, product of:
4.7224083 = idf(docFreq=1068, maxDocs=44218)
0.06252774 = queryNorm
0.3130544 = fieldWeight in 248, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.7224083 = idf(docFreq=1068, maxDocs=44218)
0.046875 = fieldNorm(doc=248)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Editor
- Deutsche Forschungsgemeinschaft / Ausschuss für Wissenschaftliche Bibliotheken und Informationssysteme
-
Maaten, L. van den: Accelerating t-SNE using Tree-Based Algorithms (2014)
0.02
0.022768933 = product of:
0.045537867 = sum of:
0.045537867 = product of:
0.13661359 = sum of:
0.13661359 = weight(_text_:objects in 3886) [ClassicSimilarity], result of:
0.13661359 = score(doc=3886,freq=2.0), product of:
0.33233938 = queryWeight, product of:
5.315071 = idf(docFreq=590, maxDocs=44218)
0.06252774 = queryNorm
0.41106653 = fieldWeight in 3886, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.315071 = idf(docFreq=590, maxDocs=44218)
0.0546875 = fieldNorm(doc=3886)
0.33333334 = coord(1/3)
0.5 = coord(1/2)
- Abstract
- The paper investigates the acceleration of t-SNE-an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots-using two tree-based algorithms. In particular, the paper develops variants of the Barnes-Hut algorithm and of the dual-tree algorithm that approximate the gradient used for learning t-SNE embeddings in O(N*logN). Our experiments show that the resulting algorithms substantially accelerate t-SNE, and that they make it possible to learn embeddings of data sets with millions of objects. Somewhat counterintuitively, the Barnes-Hut variant of t-SNE appears to outperform the dual-tree variant.
-
Loh, S.; Oliveira, J.P.M. de; Gastal, F.L.: Knowledge discovery in textual documentation : qualitative and quantitative analyses (2001)
0.02
0.01951623 = product of:
0.03903246 = sum of:
0.03903246 = product of:
0.11709737 = sum of:
0.11709737 = weight(_text_:objects in 4482) [ClassicSimilarity], result of:
0.11709737 = score(doc=4482,freq=2.0), product of:
0.33233938 = queryWeight, product of:
5.315071 = idf(docFreq=590, maxDocs=44218)
0.06252774 = queryNorm
0.35234275 = fieldWeight in 4482, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.315071 = idf(docFreq=590, maxDocs=44218)
0.046875 = fieldNorm(doc=4482)
0.33333334 = coord(1/3)
0.5 = coord(1/2)
- Abstract
- This paper presents an approach for performing knowledge discovery in texts through qualitative and quantitative analyses of high-level textual characteristics. Instead of applying mining techniques on attribute values, terms or keywords extracted from texts, the discovery process works over conceptss identified in texts. Concepts represent real world events and objects, and they help the user to understand ideas, trends, thoughts, opinions and intentions present in texts. The approach combines a quasi-automatic categorisation task (for qualitative analysis) with a mining process (for quantitative analysis). The goal is to find new and useful knowledge inside a textual collection through the use of mining techniques applied over concepts (representing text content). In this paper, an application of the approach to medical records of a psychiatric hospital is presented. The approach helps physicians to extract knowledge about patients and diseases. This knowledge may be used for epidemiological studies, for training professionals and it may be also used to support physicians to diagnose and evaluate diseases.
-
Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997)
0.02
0.01694329 = product of:
0.03388658 = sum of:
0.03388658 = product of:
0.06777316 = sum of:
0.06777316 = weight(_text_:22 in 1737) [ClassicSimilarity], result of:
0.06777316 = score(doc=1737,freq=2.0), product of:
0.21896149 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.06252774 = 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)
0.5 = coord(1/2)
- Date
- 22.11.1998 18:57:22
-
Lusti, M.: Data Warehousing and Data Mining : Eine Einführung in entscheidungsunterstützende Systeme (1999)
0.02
0.01694329 = product of:
0.03388658 = sum of:
0.03388658 = product of:
0.06777316 = sum of:
0.06777316 = weight(_text_:22 in 4261) [ClassicSimilarity], result of:
0.06777316 = score(doc=4261,freq=2.0), product of:
0.21896149 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.06252774 = 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)
0.5 = coord(1/2)
- Date
- 17. 7.2002 19:22:06
-
Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997)
0.02
0.01694329 = product of:
0.03388658 = sum of:
0.03388658 = product of:
0.06777316 = sum of:
0.06777316 = weight(_text_:22 in 1270) [ClassicSimilarity], result of:
0.06777316 = score(doc=1270,freq=2.0), product of:
0.21896149 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.06252774 = 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)
0.5 = coord(1/2)
- Source
- Information systems. 22(1997) nos.5/6, S.333-347
-
Ku, L.-W.; Chen, H.-H.: Mining opinions from the Web : beyond relevance retrieval (2007)
0.02
0.016263526 = product of:
0.032527052 = sum of:
0.032527052 = product of:
0.09758115 = sum of:
0.09758115 = weight(_text_:objects in 605) [ClassicSimilarity], result of:
0.09758115 = score(doc=605,freq=2.0), product of:
0.33233938 = queryWeight, product of:
5.315071 = idf(docFreq=590, maxDocs=44218)
0.06252774 = queryNorm
0.29361898 = fieldWeight in 605, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.315071 = idf(docFreq=590, maxDocs=44218)
0.0390625 = fieldNorm(doc=605)
0.33333334 = coord(1/3)
0.5 = coord(1/2)
- Abstract
- Documents discussing public affairs, common themes, interesting products, and so on, are reported and distributed on the Web. Positive and negative opinions embedded in documents are useful references and feedbacks for governments to improve their services, for companies to market their products, and for customers to purchase their objects. Web opinion mining aims to extract, summarize, and track various aspects of subjective information on the Web. Mining subjective information enables traditional information retrieval (IR) systems to retrieve more data from human viewpoints and provide information with finer granularity. Opinion extraction identifies opinion holders, extracts the relevant opinion sentences, and decides their polarities. Opinion summarization recognizes the major events embedded in documents and summarizes the supportive and the nonsupportive evidence. Opinion tracking captures subjective information from various genres and monitors the developments of opinions from spatial and temporal dimensions. To demonstrate and evaluate the proposed opinion mining algorithms, news and bloggers' articles are adopted. Documents in the evaluation corpora are tagged in different granularities from words, sentences to documents. In the experiments, positive and negative sentiment words and their weights are mined on the basis of Chinese word structures. The f-measure is 73.18% and 63.75% for verbs and nouns, respectively. Utilizing the sentiment words mined together with topical words, we achieve f-measure 62.16% at the sentence level and 74.37% at the document level.
-
Maaten, L. van den; Hinton, G.: Visualizing data using t-SNE (2008)
0.02
0.016263526 = product of:
0.032527052 = sum of:
0.032527052 = product of:
0.09758115 = sum of:
0.09758115 = weight(_text_:objects in 3888) [ClassicSimilarity], result of:
0.09758115 = score(doc=3888,freq=2.0), product of:
0.33233938 = queryWeight, product of:
5.315071 = idf(docFreq=590, maxDocs=44218)
0.06252774 = queryNorm
0.29361898 = fieldWeight in 3888, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.315071 = idf(docFreq=590, maxDocs=44218)
0.0390625 = fieldNorm(doc=3888)
0.33333334 = coord(1/3)
0.5 = coord(1/2)
- Abstract
- We present a new technique called "t-SNE" that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large data sets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of data sets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the data sets.
-
Heyer, G.; Quasthoff, U.; Wittig, T.: Text Mining : Wissensrohstoff Text. Konzepte, Algorithmen, Ergebnisse (2006)
0.02
0.015406529 = product of:
0.030813059 = sum of:
0.030813059 = product of:
0.061626118 = sum of:
0.061626118 = weight(_text_:deutsche in 5218) [ClassicSimilarity], result of:
0.061626118 = score(doc=5218,freq=2.0), product of:
0.2952815 = queryWeight, product of:
4.7224083 = idf(docFreq=1068, maxDocs=44218)
0.06252774 = queryNorm
0.20870294 = fieldWeight in 5218, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.7224083 = idf(docFreq=1068, maxDocs=44218)
0.03125 = fieldNorm(doc=5218)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- Ein großer Teil des Weltwissens befindet sich in Form digitaler Texte im Internet oder in Intranets. Heutige Suchmaschinen nutzen diesen Wissensrohstoff nur rudimentär: Sie können semantische Zusammen-hänge nur bedingt erkennen. Alle warten auf das semantische Web, in dem die Ersteller von Text selbst die Semantik einfügen. Das wird aber noch lange dauern. Es gibt jedoch eine Technologie, die es bereits heute ermöglicht semantische Zusammenhänge in Rohtexten zu analysieren und aufzubereiten. Das Forschungsgebiet "Text Mining" ermöglicht es mit Hilfe statistischer und musterbasierter Verfahren, Wissen aus Texten zu extrahieren, zu verarbeiten und zu nutzen. Hier wird die Basis für die Suchmaschinen der Zukunft gelegt. Das erste deutsche Lehrbuch zu einer bahnbrechenden Technologie: Text Mining: Wissensrohstoff Text Konzepte, Algorithmen, Ergebnisse Ein großer Teil des Weltwissens befindet sich in Form digitaler Texte im Internet oder in Intranets. Heutige Suchmaschinen nutzen diesen Wissensrohstoff nur rudimentär: Sie können semantische Zusammen-hänge nur bedingt erkennen. Alle warten auf das semantische Web, in dem die Ersteller von Text selbst die Semantik einfügen. Das wird aber noch lange dauern. Es gibt jedoch eine Technologie, die es bereits heute ermöglicht semantische Zusammenhänge in Rohtexten zu analysieren und aufzubereiten. Das For-schungsgebiet "Text Mining" ermöglicht es mit Hilfe statistischer und musterbasierter Verfahren, Wissen aus Texten zu extrahieren, zu verarbeiten und zu nutzen. Hier wird die Basis für die Suchmaschinen der Zukunft gelegt. Was fällt Ihnen bei dem Wort "Stich" ein? Die einen denken an Tennis, die anderen an Skat. Die verschiedenen Zusammenhänge können durch Text Mining automatisch ermittelt und in Form von Wortnetzen dargestellt werden. Welche Begriffe stehen am häufigsten links und rechts vom Wort "Festplatte"? Welche Wortformen und Eigennamen treten seit 2001 neu in der deutschen Sprache auf? Text Mining beantwortet diese und viele weitere Fragen. Tauchen Sie mit diesem Lehrbuch ein in eine neue, faszinierende Wissenschaftsdisziplin und entdecken Sie neue, bisher unbekannte Zusammenhänge und Sichtweisen. Sehen Sie, wie aus dem Wissensrohstoff Text Wissen wird! Dieses Lehrbuch richtet sich sowohl an Studierende als auch an Praktiker mit einem fachlichen Schwerpunkt in der Informatik, Wirtschaftsinformatik und/oder Linguistik, die sich über die Grundlagen, Verfahren und Anwendungen des Text Mining informieren möchten und Anregungen für die Implementierung eigener Anwendungen suchen. Es basiert auf Arbeiten, die während der letzten Jahre an der Abteilung Automatische Sprachverarbeitung am Institut für Informatik der Universität Leipzig unter Leitung von Prof. Dr. Heyer entstanden sind. Eine Fülle praktischer Beispiele von Text Mining-Konzepten und -Algorithmen verhelfen dem Leser zu einem umfassenden, aber auch detaillierten Verständnis der Grundlagen und Anwendungen des Text Mining. Folgende Themen werden behandelt: Wissen und Text Grundlagen der Bedeutungsanalyse Textdatenbanken Sprachstatistik Clustering Musteranalyse Hybride Verfahren Beispielanwendungen Anhänge: Statistik und linguistische Grundlagen 360 Seiten, 54 Abb., 58 Tabellen und 95 Glossarbegriffe Mit kostenlosen e-learning-Kurs "Schnelleinstieg: Sprachstatistik" Zusätzlich zum Buch gibt es in Kürze einen Online-Zertifikats-Kurs mit Mentor- und Tutorunterstützung.
-
Hofstede, A.H.M. ter; Proper, H.A.; Van der Weide, T.P.: Exploiting fact verbalisation in conceptual information modelling (1997)
0.01
0.0148253795 = product of:
0.029650759 = sum of:
0.029650759 = product of:
0.059301518 = sum of:
0.059301518 = weight(_text_:22 in 2908) [ClassicSimilarity], result of:
0.059301518 = score(doc=2908,freq=2.0), product of:
0.21896149 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.06252774 = 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)
0.5 = coord(1/2)
- Source
- Information systems. 22(1997) nos.5/6, S.349-385
-
Lackes, R.; Tillmanns, C.: Data Mining für die Unternehmenspraxis : Entscheidungshilfen und Fallstudien mit führenden Softwarelösungen (2006)
0.01
0.012707468 = product of:
0.025414936 = sum of:
0.025414936 = product of:
0.050829872 = sum of:
0.050829872 = weight(_text_:22 in 1383) [ClassicSimilarity], result of:
0.050829872 = score(doc=1383,freq=2.0), product of:
0.21896149 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.06252774 = 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)
0.5 = coord(1/2)
- Date
- 22. 3.2008 14:46:06
-
Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013)
0.01
0.010589557 = product of:
0.021179114 = sum of:
0.021179114 = product of:
0.042358227 = sum of:
0.042358227 = weight(_text_:22 in 668) [ClassicSimilarity], result of:
0.042358227 = score(doc=668,freq=2.0), product of:
0.21896149 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.06252774 = 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)
0.5 = coord(1/2)
- Date
- 22. 3.2013 19:43:01
-
Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015)
0.01
0.010589557 = product of:
0.021179114 = sum of:
0.021179114 = product of:
0.042358227 = sum of:
0.042358227 = weight(_text_:22 in 1605) [ClassicSimilarity], result of:
0.042358227 = score(doc=1605,freq=2.0), product of:
0.21896149 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.06252774 = 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(1/2)
- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
-
Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019)
0.01
0.010589557 = product of:
0.021179114 = sum of:
0.021179114 = product of:
0.042358227 = sum of:
0.042358227 = weight(_text_:22 in 5011) [ClassicSimilarity], result of:
0.042358227 = score(doc=5011,freq=2.0), product of:
0.21896149 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.06252774 = queryNorm
0.19345059 = fieldWeight in 5011, 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=5011)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Date
- 7. 3.2019 16:32:22
-
Peters, G.; Gaese, V.: ¬Das DocCat-System in der Textdokumentation von G+J (2003)
0.01
0.008471645 = product of:
0.01694329 = sum of:
0.01694329 = product of:
0.03388658 = sum of:
0.03388658 = weight(_text_:22 in 1507) [ClassicSimilarity], result of:
0.03388658 = score(doc=1507,freq=2.0), product of:
0.21896149 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.06252774 = queryNorm
0.15476047 = fieldWeight in 1507, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.03125 = fieldNorm(doc=1507)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Date
- 22. 4.2003 11:45:36
-
Hölzig, C.: Google spürt Grippewellen auf : Die neue Anwendung ist bisher auf die USA beschränkt (2008)
0.01
0.008471645 = product of:
0.01694329 = sum of:
0.01694329 = product of:
0.03388658 = sum of:
0.03388658 = weight(_text_:22 in 2403) [ClassicSimilarity], result of:
0.03388658 = score(doc=2403,freq=2.0), product of:
0.21896149 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.06252774 = queryNorm
0.15476047 = fieldWeight in 2403, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.03125 = fieldNorm(doc=2403)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Date
- 3. 5.1997 8:44:22
