Search (201 results, page 1 of 11)

  • × theme_ss:"Automatisches Klassifizieren"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.08 
    0.08014647 = sum of:
  0.05381863 = product of:
    0.21527451 = sum of:
      0.21527451 = weight(_text_:3a in 562) [ClassicSimilarity], result of:
        0.21527451 = score(doc=562,freq=2.0), product of:
          0.38303843 = queryWeight, product of:
            8.478011 = idf(docFreq=24, maxDocs=44218)
            0.045180224 = queryNorm
          0.56201804 = fieldWeight in 562, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            8.478011 = idf(docFreq=24, maxDocs=44218)
            0.046875 = fieldNorm(doc=562)
    0.25 = coord(1/4)
  0.007963953 = weight(_text_:a in 562) [ClassicSimilarity], result of:
    0.007963953 = score(doc=562,freq=8.0), product of:
      0.05209492 = queryWeight, product of:
        1.153047 = idf(docFreq=37942, maxDocs=44218)
        0.045180224 = queryNorm
      0.15287387 = fieldWeight in 562, product of:
        2.828427 = tf(freq=8.0), with freq of:
          8.0 = termFreq=8.0
        1.153047 = idf(docFreq=37942, maxDocs=44218)
        0.046875 = fieldNorm(doc=562)
  0.01836389 = product of:
    0.03672778 = sum of:
      0.03672778 = weight(_text_:22 in 562) [ClassicSimilarity], result of:
        0.03672778 = score(doc=562,freq=2.0), product of:
          0.15821345 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.045180224 = queryNorm
          0.23214069 = fieldWeight in 562, 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=562)
    0.5 = coord(1/2)
    Abstract
    Document representations for text classification are typically based on the classical Bag-Of-Words paradigm. This approach comes with deficiencies that motivate the integration of features on a higher semantic level than single words. In this paper we propose an enhancement of the classical document representation through concepts extracted from background knowledge. Boosting is used for actual classification. Experimental evaluations on two well known text corpora support our approach through consistent improvement of the results.
    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
    Type
    a
  2. Guerrero-Bote, V.P.; Moya Anegón, F. de; Herrero Solana, V.: Document organization using Kohonen's algorithm (2002) 0.03 
    0.032498594 = product of:
  0.04874789 = sum of:
    0.01187196 = weight(_text_:a in 2564) [ClassicSimilarity], result of:
      0.01187196 = score(doc=2564,freq=10.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.22789092 = fieldWeight in 2564, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0625 = fieldNorm(doc=2564)
    0.03687593 = product of:
      0.07375186 = sum of:
        0.07375186 = weight(_text_:de in 2564) [ClassicSimilarity], result of:
          0.07375186 = score(doc=2564,freq=2.0), product of:
            0.19416152 = queryWeight, product of:
              4.297489 = idf(docFreq=1634, maxDocs=44218)
              0.045180224 = queryNorm
            0.37984797 = fieldWeight in 2564, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.297489 = idf(docFreq=1634, maxDocs=44218)
              0.0625 = fieldNorm(doc=2564)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    The classification of documents from a bibliographic database is a task that is linked to processes of information retrieval based on partial matching. A method is described of vectorizing reference documents from LISA which permits their topological organization using Kohonen's algorithm. As an example a map is generated of 202 documents from LISA, and an analysis is made of the possibilities of this type of neural network with respect to the development of information retrieval systems based on graphical browsing.
    Type
    a
  3. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.03 
    0.029794488 = product of:
  0.04469173 = sum of:
    0.007963953 = weight(_text_:a in 1046) [ClassicSimilarity], result of:
      0.007963953 = score(doc=1046,freq=2.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.15287387 = fieldWeight in 1046, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.09375 = fieldNorm(doc=1046)
    0.03672778 = product of:
      0.07345556 = sum of:
        0.07345556 = weight(_text_:22 in 1046) [ClassicSimilarity], result of:
          0.07345556 = score(doc=1046,freq=2.0), product of:
            0.15821345 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045180224 = queryNorm
            0.46428138 = fieldWeight in 1046, 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=1046)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Date
    5. 5.2003 14:17:22
    Type
    a
  4. Cortez, E.; Herrera, M.R.; Silva, A.S. da; Moura, E.S. de; Neubert, M.: Lightweight methods for large-scale product categorization (2011) 0.02 
    0.024940504 = product of:
  0.037410755 = sum of:
    0.009753809 = weight(_text_:a in 4758) [ClassicSimilarity], result of:
      0.009753809 = score(doc=4758,freq=12.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.18723148 = fieldWeight in 4758, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=4758)
    0.027656946 = product of:
      0.055313893 = sum of:
        0.055313893 = weight(_text_:de in 4758) [ClassicSimilarity], result of:
          0.055313893 = score(doc=4758,freq=2.0), product of:
            0.19416152 = queryWeight, product of:
              4.297489 = idf(docFreq=1634, maxDocs=44218)
              0.045180224 = queryNorm
            0.28488597 = fieldWeight in 4758, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.297489 = idf(docFreq=1634, maxDocs=44218)
              0.046875 = fieldNorm(doc=4758)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    In this article, we present a study about classification methods for large-scale categorization of product offers on e-shopping web sites. We present a study about the performance of previously proposed approaches and deployed a probabilistic approach to model the classification problem. We also studied an alternative way of modeling information about the description of product offers and investigated the usage of price and store of product offers as features adopted in the classification process. Our experiments used two collections of over a million product offers previously categorized by human editors and taxonomies of hundreds of categories from a real e-shopping web site. In these experiments, our method achieved an improvement of up to 9% in the quality of the categorization in comparison with the best baseline we have found.
    Type
    a
  5. HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016) 0.02 
    0.02482874 = product of:
  0.03724311 = sum of:
    0.0066366266 = weight(_text_:a in 2748) [ClassicSimilarity], result of:
      0.0066366266 = score(doc=2748,freq=2.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.12739488 = fieldWeight in 2748, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.078125 = fieldNorm(doc=2748)
    0.030606484 = product of:
      0.061212968 = sum of:
        0.061212968 = weight(_text_:22 in 2748) [ClassicSimilarity], result of:
          0.061212968 = score(doc=2748,freq=2.0), product of:
            0.15821345 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045180224 = queryNorm
            0.38690117 = fieldWeight in 2748, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=2748)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Date
    1. 2.2016 18:25:22
    Type
    a
  6. Yoon, Y.; Lee, C.; Lee, G.G.: ¬An effective procedure for constructing a hierarchical text classification system (2006) 0.02 
    0.023574304 = product of:
  0.035361454 = sum of:
    0.013936916 = weight(_text_:a in 5273) [ClassicSimilarity], result of:
      0.013936916 = score(doc=5273,freq=18.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.26752928 = fieldWeight in 5273, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5273)
    0.02142454 = product of:
      0.04284908 = sum of:
        0.04284908 = weight(_text_:22 in 5273) [ClassicSimilarity], result of:
          0.04284908 = score(doc=5273,freq=2.0), product of:
            0.15821345 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045180224 = queryNorm
            0.2708308 = fieldWeight in 5273, 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=5273)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    In text categorization tasks, classification on some class hierarchies has better results than in cases without the hierarchy. Currently, because a large number of documents are divided into several subgroups in a hierarchy, we can appropriately use a hierarchical classification method. However, we have no systematic method to build a hierarchical classification system that performs well with large collections of practical data. In this article, we introduce a new evaluation scheme for internal node classifiers, which can be used effectively to develop a hierarchical classification system. We also show that our method for constructing the hierarchical classification system is very effective, especially for the task of constructing classifiers applied to hierarchy tree with a lot of levels.
    Date
    22. 7.2006 16:24:52
    Type
    a
  7. Calado, P.; Cristo, M.; Gonçalves, M.A.; Moura, E.S. de; Ribeiro-Neto, B.; Ziviani, N.: Link-based similarity measures for the classification of Web documents (2006) 0.02 
    0.021217927 = product of:
  0.03182689 = sum of:
    0.008779433 = weight(_text_:a in 4921) [ClassicSimilarity], result of:
      0.008779433 = score(doc=4921,freq=14.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.1685276 = fieldWeight in 4921, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4921)
    0.023047457 = product of:
      0.046094913 = sum of:
        0.046094913 = weight(_text_:de in 4921) [ClassicSimilarity], result of:
          0.046094913 = score(doc=4921,freq=2.0), product of:
            0.19416152 = queryWeight, product of:
              4.297489 = idf(docFreq=1634, maxDocs=44218)
              0.045180224 = queryNorm
            0.23740499 = fieldWeight in 4921, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.297489 = idf(docFreq=1634, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4921)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Traditional text-based document classifiers tend to perform poorly an the Web. Text in Web documents is usually noisy and often does not contain enough information to determine their topic. However, the Web provides a different source that can be useful to document classification: its hyperlink structure. In this work, the authors evaluate how the link structure of the Web can be used to determine a measure of similarity appropriate for document classification. They experiment with five different similarity measures and determine their adequacy for predicting the topic of a Web page. Tests performed an a Web directory Show that link information alone allows classifying documents with an average precision of 86%. Further, when combined with a traditional textbased classifier, precision increases to values of up to 90%, representing gains that range from 63 to 132% over the use of text-based classification alone. Because the measures proposed in this article are straightforward to compute, they provide a practical and effective solution for Web classification and related information retrieval tasks. Further, the authors provide an important set of guidelines an how link structure can be used effectively to classify Web documents.
    Type
    a
  8. Jenkins, C.: Automatic classification of Web resources using Java and Dewey Decimal Classification (1998) 0.02 
    0.021208337 = product of:
  0.031812504 = sum of:
    0.010387965 = weight(_text_:a in 1673) [ClassicSimilarity], result of:
      0.010387965 = score(doc=1673,freq=10.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.19940455 = fieldWeight in 1673, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1673)
    0.02142454 = product of:
      0.04284908 = sum of:
        0.04284908 = weight(_text_:22 in 1673) [ClassicSimilarity], result of:
          0.04284908 = score(doc=1673,freq=2.0), product of:
            0.15821345 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045180224 = queryNorm
            0.2708308 = fieldWeight in 1673, 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=1673)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    The Wolverhampton Web Library (WWLib) is a WWW search engine that provides access to UK based information. The experimental version developed in 1995, was a success but highlighted the need for a much higher degree of automation. An interesting feature of the experimental WWLib was that it organised information according to DDC. Discusses the advantages of classification and describes the automatic classifier that is being developed in Java as part of the new, fully automated WWLib
    Date
    1. 8.1996 22:08:06
    Footnote
    Contribution to a special issue devoted to the Proceedings of the 7th International World Wide Web Conference, held 14-18 April 1998, Brisbane, Australia; vgl. auch: http://www7.scu.edu.au/programme/posters/1846/com1846.htm.
    Type
    a
  9. Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007) 0.02 
    0.021208337 = product of:
  0.031812504 = sum of:
    0.010387965 = weight(_text_:a in 2560) [ClassicSimilarity], result of:
      0.010387965 = score(doc=2560,freq=10.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.19940455 = fieldWeight in 2560, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2560)
    0.02142454 = product of:
      0.04284908 = sum of:
        0.04284908 = weight(_text_:22 in 2560) [ClassicSimilarity], result of:
          0.04284908 = score(doc=2560,freq=2.0), product of:
            0.15821345 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045180224 = 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)
  0.6666667 = coord(2/3)
    Abstract
    The proliferation of digital resources and their integration into a traditional library setting has created a pressing need for an automated tool that organizes textual information based on library classification schemes. Automated text classification is a research field of developing tools, methods, and models to automate text classification. This article describes the current popular approach for text classification and major text classification projects and applications that are based on library classification schemes. Related issues and challenges are discussed, and a number of considerations for the challenges are examined.
    Date
    22. 9.2008 18:31:54
    Type
    a
  10. Egbert, J.; Biber, D.; Davies, M.: Developing a bottom-up, user-based method of web register classification (2015) 0.02 
    0.021047074 = product of:
  0.03157061 = sum of:
    0.013206721 = weight(_text_:a in 2158) [ClassicSimilarity], result of:
      0.013206721 = score(doc=2158,freq=22.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.25351265 = fieldWeight in 2158, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=2158)
    0.01836389 = product of:
      0.03672778 = sum of:
        0.03672778 = weight(_text_:22 in 2158) [ClassicSimilarity], result of:
          0.03672778 = score(doc=2158,freq=2.0), product of:
            0.15821345 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045180224 = queryNorm
            0.23214069 = fieldWeight in 2158, 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=2158)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    This paper introduces a project to develop a reliable, cost-effective method for classifying Internet texts into register categories, and apply that approach to the analysis of a large corpus of web documents. To date, the project has proceeded in 2 key phases. First, we developed a bottom-up method for web register classification, asking end users of the web to utilize a decision-tree survey to code relevant situational characteristics of web documents, resulting in a bottom-up identification of register and subregister categories. We present details regarding the development and testing of this method through a series of 10 pilot studies. Then, in the second phase of our project we applied this procedure to a corpus of 53,000 web documents. An analysis of the results demonstrates the effectiveness of these methods for web register classification and provides a preliminary description of the types and distribution of registers on the web.
    Date
    4. 8.2015 19:22:04
    Type
    a
  11. Ribeiro-Neto, B.; Laender, A.H.F.; Lima, L.R.S. de: ¬An experimental study in automatically categorizing medical documents (2001) 0.02 
    0.020783756 = product of:
  0.031175632 = sum of:
    0.008128175 = weight(_text_:a in 5702) [ClassicSimilarity], result of:
      0.008128175 = score(doc=5702,freq=12.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.15602624 = fieldWeight in 5702, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5702)
    0.023047457 = product of:
      0.046094913 = sum of:
        0.046094913 = weight(_text_:de in 5702) [ClassicSimilarity], result of:
          0.046094913 = score(doc=5702,freq=2.0), product of:
            0.19416152 = queryWeight, product of:
              4.297489 = idf(docFreq=1634, maxDocs=44218)
              0.045180224 = queryNorm
            0.23740499 = fieldWeight in 5702, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.297489 = idf(docFreq=1634, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5702)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    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
    Type
    a
  12. Liu, R.-L.: Context recognition for hierarchical text classification (2009) 0.02 
    0.01926614 = product of:
  0.028899208 = sum of:
    0.010535319 = weight(_text_:a in 2760) [ClassicSimilarity], result of:
      0.010535319 = score(doc=2760,freq=14.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.20223314 = fieldWeight in 2760, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=2760)
    0.01836389 = product of:
      0.03672778 = sum of:
        0.03672778 = weight(_text_:22 in 2760) [ClassicSimilarity], result of:
          0.03672778 = score(doc=2760,freq=2.0), product of:
            0.15821345 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045180224 = queryNorm
            0.23214069 = fieldWeight in 2760, 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=2760)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Information is often organized as a text hierarchy. A hierarchical text-classification system is thus essential for the management, sharing, and dissemination of information. It aims to automatically classify each incoming document into zero, one, or several categories in the text hierarchy. In this paper, we present a technique called CRHTC (context recognition for hierarchical text classification) that performs hierarchical text classification by recognizing the context of discussion (COD) of each category. A category's COD is governed by its ancestor categories, whose contents indicate contextual backgrounds of the category. A document may be classified into a category only if its content matches the category's COD. CRHTC does not require any trials to manually set parameters, and hence is more portable and easier to implement than other methods. It is empirically evaluated under various conditions. The results show that CRHTC achieves both better and more stable performance than several hierarchical and nonhierarchical text-classification methodologies.
    Date
    22. 3.2009 19:11:54
    Type
    a
  13. Dubin, D.: Dimensions and discriminability (1998) 0.02 
    0.018662978 = product of:
  0.027994465 = sum of:
    0.0065699257 = weight(_text_:a in 2338) [ClassicSimilarity], result of:
      0.0065699257 = score(doc=2338,freq=4.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.12611452 = fieldWeight in 2338, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2338)
    0.02142454 = product of:
      0.04284908 = sum of:
        0.04284908 = weight(_text_:22 in 2338) [ClassicSimilarity], result of:
          0.04284908 = score(doc=2338,freq=2.0), product of:
            0.15821345 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045180224 = queryNorm
            0.2708308 = fieldWeight in 2338, 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=2338)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Visualization interfaces can improve subject access by highlighting the inclusion of document representation components in similarity and discrimination relationships. Within a set of retrieved documents, what kinds of groupings can index terms and subject headings make explicit? The role of controlled vocabulary in classifying search output is examined
    Date
    22. 9.1997 19:16:05
    Type
    a
  14. Reiner, U.: Automatic analysis of DDC notations (2007) 0.02 
    0.018437965 = product of:
  0.055313893 = sum of:
    0.055313893 = product of:
      0.110627785 = sum of:
        0.110627785 = weight(_text_:de in 118) [ClassicSimilarity], result of:
          0.110627785 = score(doc=118,freq=2.0), product of:
            0.19416152 = queryWeight, product of:
              4.297489 = idf(docFreq=1634, maxDocs=44218)
              0.045180224 = queryNorm
            0.56977195 = fieldWeight in 118, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.297489 = idf(docFreq=1634, maxDocs=44218)
              0.09375 = fieldNorm(doc=118)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)
    Source
    http://www.nb.admin.ch/slb/slb_professionnel/projektarbeit/00729/01615/01675/index.html?lang=de
  15. Liu, R.-L.: ¬A passage extractor for classification of disease aspect information (2013) 0.02 
    0.017865479 = product of:
  0.026798218 = sum of:
    0.011494976 = weight(_text_:a in 1107) [ClassicSimilarity], result of:
      0.011494976 = score(doc=1107,freq=24.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.22065444 = fieldWeight in 1107, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1107)
    0.015303242 = product of:
      0.030606484 = sum of:
        0.030606484 = weight(_text_:22 in 1107) [ClassicSimilarity], result of:
          0.030606484 = score(doc=1107,freq=2.0), product of:
            0.15821345 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045180224 = queryNorm
            0.19345059 = fieldWeight in 1107, 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=1107)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Retrieval of disease information is often based on several key aspects such as etiology, diagnosis, treatment, prevention, and symptoms of diseases. Automatic identification of disease aspect information is thus essential. In this article, I model the aspect identification problem as a text classification (TC) problem in which a disease aspect corresponds to a category. The disease aspect classification problem poses two challenges to classifiers: (a) a medical text often contains information about multiple aspects of a disease and hence produces noise for the classifiers and (b) text classifiers often cannot extract the textual parts (i.e., passages) about the categories of interest. I thus develop a technique, PETC (Passage Extractor for Text Classification), that extracts passages (from medical texts) for the underlying text classifiers to classify. Case studies on thousands of Chinese and English medical texts show that PETC enhances a support vector machine (SVM) classifier in classifying disease aspect information. PETC also performs better than three state-of-the-art classifier enhancement techniques, including two passage extraction techniques for text classifiers and a technique that employs term proximity information to enhance text classifiers. The contribution is of significance to evidence-based medicine, health education, and healthcare decision support. PETC can be used in those application domains in which a text to be classified may have several parts about different categories.
    Date
    28.10.2013 19:22:57
    Type
    a
  16. Bock, H.-H.: Datenanalyse zur Strukturierung und Ordnung von Information (1989) 0.02 
    0.017380118 = product of:
  0.026070178 = sum of:
    0.0046456386 = weight(_text_:a in 141) [ClassicSimilarity], result of:
      0.0046456386 = score(doc=141,freq=2.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.089176424 = fieldWeight in 141, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0546875 = fieldNorm(doc=141)
    0.02142454 = product of:
      0.04284908 = sum of:
        0.04284908 = weight(_text_:22 in 141) [ClassicSimilarity], result of:
          0.04284908 = score(doc=141,freq=2.0), product of:
            0.15821345 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045180224 = queryNorm
            0.2708308 = fieldWeight in 141, 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=141)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Pages
    S.1-22
    Type
    a
  17. Zhu, W.Z.; Allen, R.B.: Document clustering using the LSI subspace signature model (2013) 0.02 
    0.016840585 = product of:
  0.025260875 = sum of:
    0.006896985 = weight(_text_:a in 690) [ClassicSimilarity], result of:
      0.006896985 = score(doc=690,freq=6.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.13239266 = fieldWeight in 690, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=690)
    0.01836389 = product of:
      0.03672778 = sum of:
        0.03672778 = weight(_text_:22 in 690) [ClassicSimilarity], result of:
          0.03672778 = score(doc=690,freq=2.0), product of:
            0.15821345 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045180224 = queryNorm
            0.23214069 = fieldWeight in 690, 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=690)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    We describe the latent semantic indexing subspace signature model (LSISSM) for semantic content representation of unstructured text. Grounded on singular value decomposition, the model represents terms and documents by the distribution signatures of their statistical contribution across the top-ranking latent concept dimensions. LSISSM matches term signatures with document signatures according to their mapping coherence between latent semantic indexing (LSI) term subspace and LSI document subspace. LSISSM does feature reduction and finds a low-rank approximation of scalable and sparse term-document matrices. Experiments demonstrate that this approach significantly improves the performance of major clustering algorithms such as standard K-means and self-organizing maps compared with the vector space model and the traditional LSI model. The unique contribution ranking mechanism in LSISSM also improves the initialization of standard K-means compared with random seeding procedure, which sometimes causes low efficiency and effectiveness of clustering. A two-stage initialization strategy based on LSISSM significantly reduces the running time of standard K-means procedures.
    Date
    23. 3.2013 13:22:36
    Type
    a
  18. Mengle, S.; Goharian, N.: Passage detection using text classification (2009) 0.02 
    0.016459234 = product of:
  0.024688851 = sum of:
    0.009385608 = weight(_text_:a in 2765) [ClassicSimilarity], result of:
      0.009385608 = score(doc=2765,freq=16.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.18016359 = fieldWeight in 2765, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2765)
    0.015303242 = product of:
      0.030606484 = sum of:
        0.030606484 = weight(_text_:22 in 2765) [ClassicSimilarity], result of:
          0.030606484 = score(doc=2765,freq=2.0), product of:
            0.15821345 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045180224 = queryNorm
            0.19345059 = fieldWeight in 2765, 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=2765)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    Passages can be hidden within a text to circumvent their disallowed transfer. Such release of compartmentalized information is of concern to all corporate and governmental organizations. Passage retrieval is well studied; we posit, however, that passage detection is not. Passage retrieval is the determination of the degree of relevance of blocks of text, namely passages, comprising a document. Rather than determining the relevance of a document in its entirety, passage retrieval determines the relevance of the individual passages. As such, modified traditional information-retrieval techniques compare terms found in user queries with the individual passages to determine a similarity score for passages of interest. In passage detection, passages are classified into predetermined categories. More often than not, passage detection techniques are deployed to detect hidden paragraphs in documents. That is, to hide information, documents are injected with hidden text into passages. Rather than matching query terms against passages to determine their relevance, using text-mining techniques, the passages are classified. Those documents with hidden passages are defined as infected. Thus, simply stated, passage retrieval is the search for passages relevant to a user query, while passage detection is the classification of passages. That is, in passage detection, passages are labeled with one or more categories from a set of predetermined categories. We present a keyword-based dynamic passage approach (KDP) and demonstrate that KDP outperforms statistically significantly (99% confidence) the other document-splitting approaches by 12% to 18% in the passage detection and passage category-prediction tasks. Furthermore, we evaluate the effects of the feature selection, passage length, ambiguous passages, and finally training-data category distribution on passage-detection accuracy.
    Date
    22. 3.2009 19:14:43
    Type
    a
  19. Pfeffer, M.: Automatische Vergabe von RVK-Notationen mittels fallbasiertem Schließen (2009) 0.01 
    0.014897244 = product of:
  0.022345865 = sum of:
    0.0039819763 = weight(_text_:a in 3051) [ClassicSimilarity], result of:
      0.0039819763 = score(doc=3051,freq=2.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.07643694 = fieldWeight in 3051, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=3051)
    0.01836389 = product of:
      0.03672778 = sum of:
        0.03672778 = weight(_text_:22 in 3051) [ClassicSimilarity], result of:
          0.03672778 = score(doc=3051,freq=2.0), product of:
            0.15821345 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045180224 = queryNorm
            0.23214069 = fieldWeight in 3051, 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=3051)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Date
    22. 8.2009 19:51:28
    Type
    a
  20. Khoo, C.S.G.; Ng, K.; Ou, S.: ¬An exploratory study of human clustering of Web pages (2003) 0.01 
    0.011227056 = product of:
  0.016840585 = sum of:
    0.0045979903 = weight(_text_:a in 2741) [ClassicSimilarity], result of:
      0.0045979903 = score(doc=2741,freq=6.0), product of:
        0.05209492 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.045180224 = queryNorm
        0.088261776 = fieldWeight in 2741, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.03125 = fieldNorm(doc=2741)
    0.012242594 = product of:
      0.024485188 = sum of:
        0.024485188 = weight(_text_:22 in 2741) [ClassicSimilarity], result of:
          0.024485188 = score(doc=2741,freq=2.0), product of:
            0.15821345 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045180224 = queryNorm
            0.15476047 = fieldWeight in 2741, 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=2741)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)
    Abstract
    This study seeks to find out how human beings cluster Web pages naturally. Twenty Web pages retrieved by the Northem Light search engine for each of 10 queries were sorted by 3 subjects into categories that were natural or meaningful to them. lt was found that different subjects clustered the same set of Web pages quite differently and created different categories. The average inter-subject similarity of the clusters created was a low 0.27. Subjects created an average of 5.4 clusters for each sorting. The categories constructed can be divided into 10 types. About 1/3 of the categories created were topical. Another 20% of the categories relate to the degree of relevance or usefulness. The rest of the categories were subject-independent categories such as format, purpose, authoritativeness and direction to other sources. The authors plan to develop automatic methods for categorizing Web pages using the common categories created by the subjects. lt is hoped that the techniques developed can be used by Web search engines to automatically organize Web pages retrieved into categories that are natural to users. 1. Introduction The World Wide Web is an increasingly important source of information for people globally because of its ease of access, the ease of publishing, its ability to transcend geographic and national boundaries, its flexibility and heterogeneity and its dynamic nature. However, Web users also find it increasingly difficult to locate relevant and useful information in this vast information storehouse. Web search engines, despite their scope and power, appear to be quite ineffective. They retrieve too many pages, and though they attempt to rank retrieved pages in order of probable relevance, often the relevant documents do not appear in the top-ranked 10 or 20 documents displayed. Several studies have found that users do not know how to use the advanced features of Web search engines, and do not know how to formulate and re-formulate queries. Users also typically exert minimal effort in performing, evaluating and refining their searches, and are unwilling to scan more than 10 or 20 items retrieved (Jansen, Spink, Bateman & Saracevic, 1998). This suggests that the conventional ranked-list display of search results does not satisfy user requirements, and that better ways of presenting and summarizing search results have to be developed. One promising approach is to group retrieved pages into clusters or categories to allow users to navigate immediately to the "promising" clusters where the most useful Web pages are likely to be located. This approach has been adopted by a number of search engines (notably Northem Light) and search agents.
    Date
    12. 9.2004 9:56:22
    Type
    a

Authors

Years

Languages

  • e 168
  • d 30
  • a 1
  • chi 1
  • More… Less…

Types

  • a 178
  • el 27
  • r 2
  • m 1
  • s 1
  • x 1
  • More… Less…

Themes