Search (45 results, page 2 of 3)

  • × theme_ss:"Automatisches Klassifizieren"
  • × year_i:[2000 TO 2010}
  1. Hung, C.-M.; Chien, L.-F.: Web-based text classification in the absence of manually labeled training documents (2007) 0.01 
    0.0077595054 = product of:
  0.031038022 = sum of:
    0.031038022 = weight(_text_:data in 87) [ClassicSimilarity], result of:
      0.031038022 = score(doc=87,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.2096163 = fieldWeight in 87, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=87)
  0.25 = coord(1/4)
    Abstract
    Most text classification techniques assume that manually labeled documents (corpora) can be easily obtained while learning text classifiers. However, labeled training documents are sometimes unavailable or inadequate even if they are available. The goal of this article is to present a self-learned approach to extract high-quality training documents from the Web when the required manually labeled documents are unavailable or of poor quality. To learn a text classifier automatically, we need only a set of user-defined categories and some highly related keywords. Extensive experiments are conducted to evaluate the performance of the proposed approach using the test set from the Reuters-21578 news data set. The experiments show that very promising results can be achieved only by using automatically extracted documents from the Web.
  2. Ozmutlu, S.; Cosar, G.C.: Analyzing the results of automatic new topic identification (2008) 0.01 
    0.0077595054 = product of:
  0.031038022 = sum of:
    0.031038022 = weight(_text_:data in 2604) [ClassicSimilarity], result of:
      0.031038022 = score(doc=2604,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.2096163 = fieldWeight in 2604, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=2604)
  0.25 = coord(1/4)
    Abstract
    Purpose - Identification of topic changes within a user search session is a key issue in content analysis of search engine user queries. Recently, various studies have focused on new topic identification/session identification of search engine transaction logs, and several problems regarding the estimation of topic shifts and continuations were observed in these studies. This study aims to analyze the reasons for the problems that were encountered as a result of applying automatic new topic identification. Design/methodology/approach - Measures, such as cleaning the data of common words and analyzing the errors of automatic new topic identification, are applied to eliminate the problems in estimating topic shifts and continuations. Findings - The findings show that the resulting errors of automatic new topic identification have a pattern, and further research is required to improve the performance of automatic new topic identification. Originality/value - Improving the performance of automatic new topic identification would be valuable to search engine designers, so that they can develop new clustering and query recommendation algorithms, as well as custom-tailored graphical user interfaces for search engine users.
  3. Miyamoto, S.: Information clustering based an fuzzy multisets (2003) 0.01 
    0.007418666 = product of:
  0.029674664 = sum of:
    0.029674664 = product of:
      0.05934933 = sum of:
        0.05934933 = weight(_text_:processing in 1071) [ClassicSimilarity], result of:
          0.05934933 = score(doc=1071,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.3130829 = fieldWeight in 1071, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1071)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Source
    Information processing and management. 39(2003) no.2, S.195-213
  4. Hu, G.; Zhou, S.; Guan, J.; Hu, X.: Towards effective document clustering : a constrained K-means based approach (2008) 0.01 
    0.007418666 = product of:
  0.029674664 = sum of:
    0.029674664 = product of:
      0.05934933 = sum of:
        0.05934933 = weight(_text_:processing in 2113) [ClassicSimilarity], result of:
          0.05934933 = score(doc=2113,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.3130829 = fieldWeight in 2113, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2113)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Source
    Information processing and management. 44(2008) no.4, S.1397-1409
  5. Ribeiro-Neto, B.; Laender, A.H.F.; Lima, L.R.S. de: ¬An experimental study in automatically categorizing medical documents (2001) 0.01 
    0.006466255 = product of:
  0.02586502 = sum of:
    0.02586502 = weight(_text_:data in 5702) [ClassicSimilarity], result of:
      0.02586502 = score(doc=5702,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.17468026 = fieldWeight in 5702, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5702)
  0.25 = coord(1/4)
    Abstract
    In this article, we evaluate the retrieval performance of an algorithm that automatically categorizes medical documents. The categorization, which consists in assigning an International Code of Disease (ICD) to the medical document under examination, is based on wellknown information retrieval techniques. The algorithm, which we proposed, operates in a fully automatic mode and requires no supervision or training data. Using a database of 20,569 documents, we verify that the algorithm attains levels of average precision in the 70-80% range for category coding and in the 60-70% range for subcategory coding. We also carefully analyze the case of those documents whose categorization is not in accordance with the one provided by the human specialists. The vast majority of them represent cases that can only be fully categorized with the assistance of a human subject (because, for instance, they require specific knowledge of a given pathology). For a slim fraction of all documents (0.77% for category coding and 1.4% for subcategory coding), the algorithm makes assignments that are clearly incorrect. However, this fraction corresponds to only one-fourth of the mistakes made by the human specialists
  6. Giorgetti, D.; Sebastiani, F.: Automating survey coding by multiclass text categorization techniques (2003) 0.01 
    0.006466255 = product of:
  0.02586502 = sum of:
    0.02586502 = weight(_text_:data in 5172) [ClassicSimilarity], result of:
      0.02586502 = score(doc=5172,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.17468026 = fieldWeight in 5172, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5172)
  0.25 = coord(1/4)
    Abstract
    In this issue Giorgetti, and Sebastiani suggest that answers to open ended questions in survey instruments can be coded automatically by creating classifiers which learn from training sets of manually coded answers. The manual effort required is only that of classifying a representative set of documents, not creating a dictionary of words that trigger an assignment. They use a naive Bayesian probabilistic learner from Mc Callum's RAINBOW package and the multi-class support vector machine learner from Hsu and Lin's BSVM package, both examples of text categorization techniques. Data from the 1996 General Social Survey by the U.S. National Opinion Research Center provided a set of answers to three questions (previously tested by Viechnicki using a dictionary approach), their associated manually assigned category codes, and a complete set of predefined category codes. The learners were run on three random disjoint subsets of the answer sets to create the classifiers and a remaining set was used as a test set. The dictionary approach is out preformed by 18% for RAINBOW and by 17% for BSVM, while the standard deviation of the results is reduced by 28% and 34% respectively over the dictionary approach.
  7. Yao, H.; Etzkorn, L.H.; Virani, S.: Automated classification and retrieval of reusable software components (2008) 0.01 
    0.006466255 = product of:
  0.02586502 = sum of:
    0.02586502 = weight(_text_:data in 1382) [ClassicSimilarity], result of:
      0.02586502 = score(doc=1382,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.17468026 = fieldWeight in 1382, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1382)
  0.25 = coord(1/4)
    Abstract
    The authors describe their research which improves software reuse by using an automated approach to semantically search for and retrieve reusable software components in large software component repositories and on the World Wide Web (WWW). Using automation and smart (semantic) techniques, their approach speeds up the search and retrieval of reusable software components, while retaining good accuracy, and therefore improves the affordability of software reuse. A program understanding of software components and natural language understanding of user queries was employed. Then the software component descriptions were compared by matching the resulting semantic representations of the user queries to the semantic representations of the software components to search for software components that best match the user queries. A proof of concept system was developed to test the authors' approach. The results of this proof of concept system were compared to human experts, and statistical analysis was performed on the collected experimental data. The results from these experiments demonstrate that this automated semantic-based approach for software reusable component classification and retrieval is successful when compared to the labor-intensive results from the experts, thus showing that this approach can significantly benefit software reuse classification and retrieval.
  8. Liu, R.-L.: Dynamic category profiling for text filtering and classification (2007) 0.01 
    0.0063588563 = product of:
  0.025435425 = sum of:
    0.025435425 = product of:
      0.05087085 = sum of:
        0.05087085 = weight(_text_:processing in 900) [ClassicSimilarity], result of:
          0.05087085 = score(doc=900,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.26835677 = fieldWeight in 900, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046875 = fieldNorm(doc=900)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Source
    Information processing and management. 43(2007) no.1, S.154-168
  9. Yoon, Y.; Lee, G.G.: Efficient implementation of associative classifiers for document classification (2007) 0.01 
    0.0063588563 = product of:
  0.025435425 = sum of:
    0.025435425 = product of:
      0.05087085 = sum of:
        0.05087085 = weight(_text_:processing in 909) [ClassicSimilarity], result of:
          0.05087085 = score(doc=909,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.26835677 = fieldWeight in 909, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046875 = fieldNorm(doc=909)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Source
    Information processing and management. 43(2007) no.2, S.393-405
  10. Denoyer, L.; Gallinari, P.: Bayesian network model for semi-structured document classification (2004) 0.01 
    0.0063588563 = product of:
  0.025435425 = sum of:
    0.025435425 = product of:
      0.05087085 = sum of:
        0.05087085 = weight(_text_:processing in 995) [ClassicSimilarity], result of:
          0.05087085 = score(doc=995,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.26835677 = fieldWeight in 995, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046875 = fieldNorm(doc=995)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Source
    Information processing and management. 40(2004) no.5, S.807-827
  11. Cosh, K.J.; Burns, R.; Daniel, T.: Content clouds : classifying content in Web 2.0 (2008) 0.01 
    0.0063588563 = product of:
  0.025435425 = sum of:
    0.025435425 = product of:
      0.05087085 = sum of:
        0.05087085 = weight(_text_:processing in 2013) [ClassicSimilarity], result of:
          0.05087085 = score(doc=2013,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.26835677 = fieldWeight in 2013, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046875 = fieldNorm(doc=2013)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Abstract
    Purpose - With increasing amounts of user generated content being produced electronically in the form of wikis, blogs, forums etc. the purpose of this paper is to investigate a new approach to classifying ad hoc content. Design/methodology/approach - The approach applies natural language processing (NLP) tools to automatically extract the content of some text, visualizing the results in a content cloud. Findings - Content clouds share the visual simplicity of a tag cloud, but display the details of an article at a different level of abstraction, providing a complimentary classification. Research limitations/implications - Provides the general approach to creating a content cloud. In the future, the process can be refined and enhanced by further evaluation of results. Further work is also required to better identify closely related articles. Practical implications - Being able to automatically classify the content generated by web users will enable others to find more appropriate content. Originality/value - The approach is original. Other researchers have produced a cloud, simply by using skiplists to filter unwanted words, this paper's approach improves this by applying appropriate NLP techniques.
  12. Malenica, M.; Smuc, T.; Snajder, J.; Basic, B.D.: Language morphology offset : text classification on a Croatian-English parallel corpus (2008) 0.01 
    0.0063588563 = product of:
  0.025435425 = sum of:
    0.025435425 = product of:
      0.05087085 = sum of:
        0.05087085 = weight(_text_:processing in 2035) [ClassicSimilarity], result of:
          0.05087085 = score(doc=2035,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.26835677 = fieldWeight in 2035, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046875 = fieldNorm(doc=2035)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Source
    Information processing and management. 44(2008) no.1, S.325-339
  13. Montesi, M.; Navarrete, T.: Classifying web genres in context : A case study documenting the web genres used by a software engineer (2008) 0.01 
    0.0063588563 = product of:
  0.025435425 = sum of:
    0.025435425 = product of:
      0.05087085 = sum of:
        0.05087085 = weight(_text_:processing in 2100) [ClassicSimilarity], result of:
          0.05087085 = score(doc=2100,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.26835677 = fieldWeight in 2100, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046875 = fieldNorm(doc=2100)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Source
    Information processing and management. 44(2008) no.4, S.1410-1430
  14. Ko, Y.; Seo, J.: Text classification from unlabeled documents with bootstrapping and feature projection techniques (2009) 0.01 
    0.0063588563 = product of:
  0.025435425 = sum of:
    0.025435425 = product of:
      0.05087085 = sum of:
        0.05087085 = weight(_text_:processing in 2452) [ClassicSimilarity], result of:
          0.05087085 = score(doc=2452,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.26835677 = fieldWeight in 2452, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046875 = fieldNorm(doc=2452)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Source
    Information processing and management. 45(2009) no.1, S.70-83
  15. Automatic classification research at OCLC (2002) 0.01 
    0.0055514094 = product of:
  0.022205638 = sum of:
    0.022205638 = product of:
      0.044411276 = sum of:
        0.044411276 = weight(_text_:22 in 1563) [ClassicSimilarity], result of:
          0.044411276 = score(doc=1563,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.2708308 = fieldWeight in 1563, 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=1563)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Date
    5. 5.2003 9:22:09
  16. Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007) 0.01 
    0.0055514094 = product of:
  0.022205638 = sum of:
    0.022205638 = product of:
      0.044411276 = sum of:
        0.044411276 = weight(_text_:22 in 2560) [ClassicSimilarity], result of:
          0.044411276 = score(doc=2560,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.2708308 = fieldWeight in 2560, 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=2560)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Date
    22. 9.2008 18:31:54
  17. Golub, K.: Automated subject classification of textual web documents (2006) 0.01 
    0.005299047 = product of:
  0.021196188 = sum of:
    0.021196188 = product of:
      0.042392377 = sum of:
        0.042392377 = weight(_text_:processing in 5600) [ClassicSimilarity], result of:
          0.042392377 = score(doc=5600,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.22363065 = fieldWeight in 5600, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5600)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Abstract
    Purpose - To provide an integrated perspective to similarities and differences between approaches to automated classification in different research communities (machine learning, information retrieval and library science), and point to problems with the approaches and automated classification as such. Design/methodology/approach - A range of works dealing with automated classification of full-text web documents are discussed. Explorations of individual approaches are given in the following sections: special features (description, differences, evaluation), application and characteristics of web pages. Findings - Provides major similarities and differences between the three approaches: document pre-processing and utilization of web-specific document characteristics is common to all the approaches; major differences are in applied algorithms, employment or not of the vector space model and of controlled vocabularies. Problems of automated classification are recognized. Research limitations/implications - The paper does not attempt to provide an exhaustive bibliography of related resources. Practical implications - As an integrated overview of approaches from different research communities with application examples, it is very useful for students in library and information science and computer science, as well as for practitioners. Researchers from one community have the information on how similar tasks are conducted in different communities. Originality/value - To the author's knowledge, no review paper on automated text classification attempted to discuss more than one community's approach from an integrated perspective.
  18. Peng, F.; Huang, X.: Machine learning for Asian language text classification (2007) 0.01 
    0.005299047 = product of:
  0.021196188 = sum of:
    0.021196188 = product of:
      0.042392377 = sum of:
        0.042392377 = weight(_text_:processing in 831) [ClassicSimilarity], result of:
          0.042392377 = score(doc=831,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.22363065 = fieldWeight in 831, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0390625 = fieldNorm(doc=831)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Abstract
    Purpose - The purpose of this research is to compare several machine learning techniques on the task of Asian language text classification, such as Chinese and Japanese where no word boundary information is available in written text. The paper advocates a simple language modeling based approach for this task. Design/methodology/approach - Naïve Bayes, maximum entropy model, support vector machines, and language modeling approaches were implemented and were applied to Chinese and Japanese text classification. To investigate the influence of word segmentation, different word segmentation approaches were investigated and applied to Chinese text. A segmentation-based approach was compared with the non-segmentation-based approach. Findings - There were two findings: the experiments show that statistical language modeling can significantly outperform standard techniques, given the same set of features; and it was found that classification with word level features normally yields improved classification performance, but that classification performance is not monotonically related to segmentation accuracy. In particular, classification performance may initially improve with increased segmentation accuracy, but eventually classification performance stops improving, and can in fact even decrease, after a certain level of segmentation accuracy. Practical implications - Apply the findings to real web text classification is ongoing work. Originality/value - The paper is very relevant to Chinese and Japanese information processing, e.g. webpage classification, web search.
  19. Lim, C.S.; Lee, K.J.; Kim, G.C.: Multiple sets of features for automatic genre classification of web documents (2005) 0.01 
    0.005299047 = product of:
  0.021196188 = sum of:
    0.021196188 = product of:
      0.042392377 = sum of:
        0.042392377 = weight(_text_:processing in 1048) [ClassicSimilarity], result of:
          0.042392377 = score(doc=1048,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.22363065 = fieldWeight in 1048, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1048)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Source
    Information processing and management. 41(2005) no.5, S.1263-1276
  20. Kwon, O.W.; Lee, J.H.: Text categorization based on k-nearest neighbor approach for web site classification (2003) 0.01 
    0.005299047 = product of:
  0.021196188 = sum of:
    0.021196188 = product of:
      0.042392377 = sum of:
        0.042392377 = weight(_text_:processing in 1070) [ClassicSimilarity], result of:
          0.042392377 = score(doc=1070,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.22363065 = fieldWeight in 1070, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1070)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
    Source
    Information processing and management. 39(2003) no.1, S.25-44

Authors

Languages

  • e 41
  • d 4

Types

  • a 42
  • el 3
  • m 1
  • s 1
  • More… Less…

Themes

Subjects