Search (11 results, page 1 of 1)

0.038383592 = product of:
  0.076767184 = sum of:
    0.020873476 = weight(_text_:retrieval in 1842) [ClassicSimilarity], result of:
      0.020873476 = score(doc=1842,freq=2.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.16710453 = fieldWeight in 1842, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1842)
    0.021389665 = weight(_text_:use in 1842) [ClassicSimilarity], result of:
      0.021389665 = score(doc=1842,freq=2.0), product of:
        0.12644777 = queryWeight, product of:
          3.0620887 = idf(docFreq=5623, maxDocs=44218)
          0.041294612 = queryNorm
        0.1691581 = fieldWeight in 1842, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0620887 = idf(docFreq=5623, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1842)
    0.024947217 = weight(_text_:of in 1842) [ClassicSimilarity], result of:
      0.024947217 = score(doc=1842,freq=40.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.38633084 = fieldWeight in 1842, product of:
          6.3245554 = tf(freq=40.0), with freq of:
            40.0 = termFreq=40.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1842)
    0.00955682 = product of:
      0.01911364 = sum of:
        0.01911364 = weight(_text_:on in 1842) [ClassicSimilarity], result of:
          0.01911364 = score(doc=1842,freq=6.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.21044704 = fieldWeight in 1842, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1842)
      0.5 = coord(1/2)
  0.5 = coord(4/8)

Abstract

Many terminology engineering processes involve the task of automatic terminology extraction: before the terminology of a given domain can be modelled, organised or standardised, important concepts (or terms) of this domain have to be identified and fed into terminological databases. These serve in further steps as a starting point for compiling dictionaries, thesauri or maybe even terminological ontologies for the domain. For the extraction of the initial concepts, extraction methods are needed that operate on specialised language texts. On the other hand, many machine learning or information retrieval applications require automatic indexing techniques. In Machine Learning applications concerned with the automatic clustering or classification of texts, often feature vectors are needed that describe the contents of a given text briefly but meaningfully. These feature vectors typically consist of a fairly small set of index terms together with weights indicating their importance. Short but meaningful descriptions of document contents as provided by good index terms are also useful to humans: some knowledge management applications (e.g. topic maps) use them as a set of basic concepts (topics). The author believes that the tasks of terminology extraction and automatic indexing have much in common and can thus benefit from the same set of basic algorithms. It is the goal of this paper to outline some methods that may be used in both contexts, but also to find the discriminating factors between the two tasks that call for the variation of parameters or application of different techniques. The discussion of these methods will be based on statistical, syntactical and especially morphological properties of (index) terms. The paper is concluded by the presentation of some qualitative and quantitative results comparing statistical and morphological methods.

Source

TKE 2005: Proc. of Terminology and Knowledge Engineering (TKE) 2005

0.029560743 = product of:
  0.07882865 = sum of:
    0.04338471 = weight(_text_:retrieval in 5599) [ClassicSimilarity], result of:
      0.04338471 = score(doc=5599,freq=6.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.34732026 = fieldWeight in 5599, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=5599)
    0.022201622 = weight(_text_:of in 5599) [ClassicSimilarity], result of:
      0.022201622 = score(doc=5599,freq=22.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.34381276 = fieldWeight in 5599, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=5599)
    0.013242318 = product of:
      0.026484637 = sum of:
        0.026484637 = weight(_text_:on in 5599) [ClassicSimilarity], result of:
          0.026484637 = score(doc=5599,freq=8.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.29160398 = fieldWeight in 5599, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.046875 = fieldNorm(doc=5599)
      0.5 = coord(1/2)
  0.375 = coord(3/8)

Abstract

Purpose - To evaluate the accuracy of conflation methods based on finite-state transducers (FSTs). Design/methodology/approach - Incorrectly lemmatized and stemmed forms may lead to the retrieval of inappropriate documents. Experimental studies to date have focused on retrieval performance, but very few on conflation performance. The process of normalization we used involved a linguistic toolbox that allowed us to construct, through graphic interfaces, electronic dictionaries represented internally by FSTs. The lexical resources developed were applied to a Spanish test corpus for merging term variants in canonical lemmatized forms. Conflation performance was evaluated in terms of an adaptation of recall and precision measures, based on accuracy and coverage, not actual retrieval. The results were compared with those obtained using a Spanish version of the Porter algorithm. Findings - The conclusion is that the main strength of lemmatization is its accuracy, whereas its main limitation is the underanalysis of variant forms. Originality/value - The report outlines the potential of transducers in their application to normalization processes.

Source

Journal of documentation. 62(2006) no.3, S.328-349

0.0264084 = product of:
  0.0704224 = sum of:
    0.04174695 = weight(_text_:retrieval in 896) [ClassicSimilarity], result of:
      0.04174695 = score(doc=896,freq=8.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.33420905 = fieldWeight in 896, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=896)
    0.02087234 = weight(_text_:of in 896) [ClassicSimilarity], result of:
      0.02087234 = score(doc=896,freq=28.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.32322758 = fieldWeight in 896, product of:
          5.2915025 = tf(freq=28.0), with freq of:
            28.0 = termFreq=28.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=896)
    0.007803111 = product of:
      0.015606222 = sum of:
        0.015606222 = weight(_text_:on in 896) [ClassicSimilarity], result of:
          0.015606222 = score(doc=896,freq=4.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.1718293 = fieldWeight in 896, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.0390625 = fieldNorm(doc=896)
      0.5 = coord(1/2)
  0.375 = coord(3/8)

Abstract

This paper deals with Swedish full text retrieval and the problem of morphological variation of query terms in the document database. The effects of combination of indexing strategies with query terms on retrieval effectiveness were studied. Three of five tested combinations involved indexing strategies that used conflation, in the form of normalization. Further, two of these three combinations used indexing strategies that employed compound splitting. Normalization and compound splitting were performed by SWETWOL, a morphological analyzer for the Swedish language. A fourth combination attempted to group related terms by right hand truncation of query terms. The four combinations were compared to each other and to a baseline combination, where no attempt was made to counteract the problem of morphological variation of query terms in the document database. The five combinations were evaluated under six different user scenarios, where each scenario simulated a certain user type. The four alternative combinations outperformed the baseline, for each user scenario. The truncation combination had the best performance under each user scenario. The main conclusion of the paper is that normalization and right hand truncation (performed by a search expert) enhanced retrieval effectiveness in comparison to the baseline. The performance of the three combinations of indexing strategies with query terms based on normalization was not far below the performance of the truncation combination.

0.020201609 = product of:
  0.053870954 = sum of:
    0.025048172 = weight(_text_:retrieval in 4476) [ClassicSimilarity], result of:
      0.025048172 = score(doc=4476,freq=2.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.20052543 = fieldWeight in 4476, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=4476)
    0.022201622 = weight(_text_:of in 4476) [ClassicSimilarity], result of:
      0.022201622 = score(doc=4476,freq=22.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.34381276 = fieldWeight in 4476, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=4476)
    0.006621159 = product of:
      0.013242318 = sum of:
        0.013242318 = weight(_text_:on in 4476) [ClassicSimilarity], result of:
          0.013242318 = score(doc=4476,freq=2.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.14580199 = fieldWeight in 4476, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.046875 = fieldNorm(doc=4476)
      0.5 = coord(1/2)
  0.375 = coord(3/8)

Abstract

This paper presents a morphological classification of languages from the IR perspective. Linguistic typology research has shown that the morphological complexity of every language in the world can be described by two variables, index of synthesis and index of fusion. These variables provide a theoretical basis for IR research handling morphological issues. A common theoretical framework is needed in particular because of the increasing significance of cross-language retrieval research and CLIR systems processing different languages. The paper elaborates the linguistic morphological typology for the purposes of IR research. It studies how the indexes of synthesis and fusion could be used as practical tools in mono- and cross-lingual IR research. The need for semantic and syntactic typologies is discussed. The paper also reviews studies made in different languages on the effects of morphology and stemming in IR.

Source

Journal of documentation. 57(2001) no.3, S.330-348

0.019306757 = product of:
  0.051484685 = sum of:
    0.025048172 = weight(_text_:retrieval in 2910) [ClassicSimilarity], result of:
      0.025048172 = score(doc=2910,freq=2.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.20052543 = fieldWeight in 2910, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=2910)
    0.014968331 = weight(_text_:of in 2910) [ClassicSimilarity], result of:
      0.014968331 = score(doc=2910,freq=10.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.23179851 = fieldWeight in 2910, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=2910)
    0.011468184 = product of:
      0.022936368 = sum of:
        0.022936368 = weight(_text_:on in 2910) [ClassicSimilarity], result of:
          0.022936368 = score(doc=2910,freq=6.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.25253648 = fieldWeight in 2910, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.046875 = fieldNorm(doc=2910)
      0.5 = coord(1/2)
  0.375 = coord(3/8)

Abstract

Due to natural language morphology, words can take on various morphological forms. Morphological normalisation - often used in information retrieval and text mining systems - conflates morphological variants of a word to a single representative form. In this paper, we describe an approach to lexicon-based inflectional normalisation. This approach is in between stemming and lemmatisation, and is suitable for morphological normalisation of inflectionally complex languages. To eliminate the immense effort required to compile the lexicon by hand, we focus on the problem of acquiring automatically an inflectional morphological lexicon from raw corpora. We propose a convenient and highly expressive morphology representation formalism on which the acquisition procedure is based. Our approach is applied to the morphologically complex Croatian language, but it should be equally applicable to other languages of similar morphological complexity. Experimental results show that our approach can be used to acquire a lexicon whose linguistic quality allows for rather good normalisation performance.

0.007669405 = product of:
  0.06135524 = sum of:
    0.06135524 = weight(_text_:retrieval in 6386) [ClassicSimilarity], result of:
      0.06135524 = score(doc=6386,freq=12.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.49118498 = fieldWeight in 6386, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=6386)
  0.125 = coord(1/8)

Abstract

Retrieval Tests sind die anerkannteste Methode, um neue Verfahren der Inhaltserschließung gegenüber traditionellen Verfahren zu rechtfertigen. Im Rahmen einer Diplomarbeit wurden zwei grundsätzlich unterschiedliche Systeme der automatischen inhaltlichen Erschließung anhand der Pressedatenbank des Verlagshauses Gruner + Jahr (G+J) getestet und evaluiert. Untersucht wurde dabei natürlichsprachliches Retrieval im Vergleich zu Booleschem Retrieval. Bei den beiden Systemen handelt es sich zum einen um Autonomy von Autonomy Inc. und DocCat, das von IBM an die Datenbankstruktur der G+J Pressedatenbank angepasst wurde. Ersteres ist ein auf natürlichsprachlichem Retrieval basierendes, probabilistisches System. DocCat demgegenüber basiert auf Booleschem Retrieval und ist ein lernendes System, das auf Grund einer intellektuell erstellten Trainingsvorlage indexiert. Methodisch geht die Evaluation vom realen Anwendungskontext der Textdokumentation von G+J aus. Die Tests werden sowohl unter statistischen wie auch qualitativen Gesichtspunkten bewertet. Ein Ergebnis der Tests ist, dass DocCat einige Mängel gegenüber der intellektuellen Inhaltserschließung aufweist, die noch behoben werden müssen, während das natürlichsprachliche Retrieval von Autonomy in diesem Rahmen und für die speziellen Anforderungen der G+J Textdokumentation so nicht einsetzbar ist

0.005834314 = product of:
  0.046674512 = sum of:
    0.046674512 = weight(_text_:retrieval in 5863) [ClassicSimilarity], result of:
      0.046674512 = score(doc=5863,freq=10.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.37365708 = fieldWeight in 5863, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5863)
  0.125 = coord(1/8)

Abstract

Retrievaltests sind die anerkannteste Methode, um neue Verfahren der Inhaltserschließung gegenüber traditionellen Verfahren zu rechtfertigen. Im Rahmen einer Diplomarbeit wurden zwei grundsätzlich unterschiedliche Systeme der automatischen inhaltlichen Erschließung anhand der Pressedatenbank des Verlagshauses Gruner + Jahr (G+J) getestet und evaluiert. Untersucht wurde dabei natürlichsprachliches Retrieval im Vergleich zu Booleschem Retrieval. Bei den beiden Systemen handelt es sich zum einen um Autonomy von Autonomy Inc. und DocCat, das von IBM an die Datenbankstruktur der G+J Pressedatenbank angepasst wurde. Ersteres ist ein auf natürlichsprachlichem Retrieval basierendes, probabilistisches System. DocCat demgegenüber basiert auf Booleschem Retrieval und ist ein lernendes System, das aufgrund einer intellektuell erstellten Trainingsvorlage indexiert. Methodisch geht die Evaluation vom realen Anwendungskontext der Textdokumentation von G+J aus. Die Tests werden sowohl unter statistischen wie auch qualitativen Gesichtspunkten bewertet. Ein Ergebnis der Tests ist, dass DocCat einige Mängel gegenüber der intellektuellen Inhaltserschließung aufweist, die noch behoben werden müssen, während das natürlichsprachliche Retrieval von Autonomy in diesem Rahmen und für die speziellen Anforderungen der G+J Textdokumentation so nicht einsetzbar ist

0.0045538945 = product of:
  0.018215578 = sum of:
    0.011594418 = weight(_text_:of in 5480) [ClassicSimilarity], result of:
      0.011594418 = score(doc=5480,freq=6.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.17955035 = fieldWeight in 5480, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=5480)
    0.006621159 = product of:
      0.013242318 = sum of:
        0.013242318 = weight(_text_:on in 5480) [ClassicSimilarity], result of:
          0.013242318 = score(doc=5480,freq=2.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.14580199 = fieldWeight in 5480, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.046875 = fieldNorm(doc=5480)
      0.5 = coord(1/2)
  0.25 = coord(2/8)

Abstract

(Automatic) document classification is generally defined as content-based assignment of one or more predefined categories to documents. Usually, machine learning, statistical pattern recognition, or neural network approaches are used to construct classifiers automatically. In this paper we thoroughly evaluate a wide variety of these methods on a document classification task for German text. We evaluate different feature construction and selection methods and various classifiers. Our main results are: (1) feature selection is necessary not only to reduce learning and classification time, but also to avoid overfitting (even for Support Vector Machines); (2) surprisingly, our morphological analysis does not improve classification quality compared to a letter 5-gram approach; (3) Support Vector Machines are significantly better than all other classification methods

0.0044978103 = product of:
  0.017991241 = sum of:
    0.012473608 = weight(_text_:of in 6029) [ClassicSimilarity], result of:
      0.012473608 = score(doc=6029,freq=10.0), product of:
        0.06457475 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.041294612 = queryNorm
        0.19316542 = fieldWeight in 6029, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6029)
    0.0055176322 = product of:
      0.0110352645 = sum of:
        0.0110352645 = weight(_text_:on in 6029) [ClassicSimilarity], result of:
          0.0110352645 = score(doc=6029,freq=2.0), product of:
            0.090823986 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.041294612 = queryNorm
            0.121501654 = fieldWeight in 6029, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.0390625 = fieldNorm(doc=6029)
      0.5 = coord(1/2)
  0.25 = coord(2/8)

Abstract

Over the past few years, temporal information processing and temporal database management have increasingly become hot topics. Nevertheless, only a few researchers have investigated these areas in the Chinese language. This lays down the objective of our research: to exploit Chinese language processing techniques for temporal information extraction and concept reasoning. In this article, we first study the mechanism for expressing time in Chinese. On the basis of the study, we then design a general frame structure for maintaining the extracted temporal concepts and propose a system for extracting time-dependent information from Hong Kong financial news. In the system, temporal knowledge is represented by different types of temporal concepts (TTC) and different temporal relations, including absolute and relative relations, which are used to correlate between action times and reference times. In analyzing a sentence, the algorithm first determines the situation related to the verb. This in turn will identify the type of temporal concept associated with the verb. After that, the relevant temporal information is extracted and the temporal relations are derived. These relations link relevant concept frames together in chronological order, which in turn provide the knowledge to fulfill users' queries, e.g., for question-answering (i.e., Q&A) applications

Source

Journal of the American Society for Information Science and technology. 52(2001) no.9, S.748-762

0.0041746954 = product of:
  0.033397563 = sum of:
    0.033397563 = weight(_text_:retrieval in 4891) [ClassicSimilarity], result of:
      0.033397563 = score(doc=4891,freq=2.0), product of:
        0.124912694 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.041294612 = queryNorm
        0.26736724 = fieldWeight in 4891, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0625 = fieldNorm(doc=4891)
  0.125 = coord(1/8)

Abstract

Nur wenige Dokumentationsmethoden werden mit dem Namen ihrer Entwickler assoziiert. Ausnahmen sind Melvil Dewey (DDC), S.R. Ranganathan (Colon Classification) - und Norbert Henrichs. Seine Textwortmethode ermöglicht die Indexierung und das Retrieval von Literatur aus Fachgebieten, die keine allseits akzeptierte Fachterminologie vorweisen, also viele Sozial- und Geisteswissenschaften, vorneweg die Philosophie. Für den Einsatz in der elektronischen Philosophie-Dokumentation hat Henrichs in den späten sechziger Jahren die Textwortmethode entworfen. Er ist damit nicht nur einer der Pioniere der Anwendung der elektronischen Datenverarbeitung in der Informationspraxis, sondern auch der Pionier bei der Dokumentation terminologisch nicht starrer Fachsprachen

0.0020980686 = product of:
  0.016784549 = sum of:
    0.016784549 = product of:
      0.033569098 = sum of:
        0.033569098 = weight(_text_:22 in 1746) [ClassicSimilarity], result of:
          0.033569098 = score(doc=1746,freq=2.0), product of:
            0.1446067 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.041294612 = queryNorm
            0.23214069 = fieldWeight in 1746, 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=1746)
      0.5 = coord(1/2)
  0.125 = coord(1/8)

Date

22. 3.2015 9:17:30