Search (5 results, page 1 of 1)

  • × year_i:[2020 TO 2030}
  • × theme_ss:"Automatisches Klassifizieren"
  1. Kragelj, M.; Borstnar, M.K.: Automatic classification of older electronic texts into the Universal Decimal Classification-UDC (2021) 0.02 
    0.023457918 = product of:
  0.046915837 = sum of:
    0.015474406 = weight(_text_:science in 175) [ClassicSimilarity], result of:
      0.015474406 = score(doc=175,freq=2.0), product of:
        0.1329271 = queryWeight, product of:
          2.6341193 = idf(docFreq=8627, maxDocs=44218)
          0.050463587 = queryNorm
        0.11641272 = fieldWeight in 175, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.6341193 = idf(docFreq=8627, maxDocs=44218)
          0.03125 = fieldNorm(doc=175)
    0.03144143 = weight(_text_:research in 175) [ClassicSimilarity], result of:
      0.03144143 = score(doc=175,freq=6.0), product of:
        0.14397179 = queryWeight, product of:
          2.8529835 = idf(docFreq=6931, maxDocs=44218)
          0.050463587 = queryNorm
        0.21838607 = fieldWeight in 175, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          2.8529835 = idf(docFreq=6931, maxDocs=44218)
          0.03125 = fieldNorm(doc=175)
  0.5 = coord(2/4)
    Abstract
    Purpose The purpose of this study is to develop a model for automated classification of old digitised texts to the Universal Decimal Classification (UDC), using machine-learning methods. Design/methodology/approach The general research approach is inherent to design science research, in which the problem of UDC assignment of the old, digitised texts is addressed by developing a machine-learning classification model. A corpus of 70,000 scholarly texts, fully bibliographically processed by librarians, was used to train and test the model, which was used for classification of old texts on a corpus of 200,000 items. Human experts evaluated the performance of the model. Findings Results suggest that machine-learning models can correctly assign the UDC at some level for almost any scholarly text. Furthermore, the model can be recommended for the UDC assignment of older texts. Ten librarians corroborated this on 150 randomly selected texts. Research limitations/implications The main limitations of this study were unavailability of labelled older texts and the limited availability of librarians. Practical implications The classification model can provide a recommendation to the librarians during their classification work; furthermore, it can be implemented as an add-on to full-text search in the library databases. Social implications The proposed methodology supports librarians by recommending UDC classifiers, thus saving time in their daily work. By automatically classifying older texts, digital libraries can provide a better user experience by enabling structured searches. These contribute to making knowledge more widely available and useable. Originality/value These findings contribute to the field of automated classification of bibliographical information with the usage of full texts, especially in cases in which the texts are old, unstructured and in which archaic language and vocabulary are used.
  2. Pech, G.; Delgado, C.; Sorella, S.P.: Classifying papers into subfields using Abstracts, Titles, Keywords and KeyWords Plus through pattern detection and optimization procedures : an application in Physics (2022) 0.02 
    0.021016954 = product of:
  0.042033907 = sum of:
    0.019343007 = weight(_text_:science in 744) [ClassicSimilarity], result of:
      0.019343007 = score(doc=744,freq=2.0), product of:
        0.1329271 = queryWeight, product of:
          2.6341193 = idf(docFreq=8627, maxDocs=44218)
          0.050463587 = queryNorm
        0.1455159 = fieldWeight in 744, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.6341193 = idf(docFreq=8627, maxDocs=44218)
          0.0390625 = fieldNorm(doc=744)
    0.0226909 = weight(_text_:research in 744) [ClassicSimilarity], result of:
      0.0226909 = score(doc=744,freq=2.0), product of:
        0.14397179 = queryWeight, product of:
          2.8529835 = idf(docFreq=6931, maxDocs=44218)
          0.050463587 = queryNorm
        0.15760657 = fieldWeight in 744, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.8529835 = idf(docFreq=6931, maxDocs=44218)
          0.0390625 = fieldNorm(doc=744)
  0.5 = coord(2/4)
    Abstract
    Classifying papers according to the fields of knowledge is critical to clearly understand the dynamics of scientific (sub)fields, their leading questions, and trends. Most studies rely on journal categories defined by popular databases such as WoS or Scopus, but some experts find that those categories may not correctly map the existing subfields nor identify the subfield of a specific article. This study addresses the classification problem using data from each paper (Abstract, Title, Keywords, and the KeyWords Plus) and the help of experts to identify the existing subfields and journals exclusive of each subfield. These "exclusive journals" are critical to obtain, through a pattern detection procedure that uses machine learning techniques (from software NVivo), a list of the frequent terms that are specific to each subfield. With that list of terms and with the help of optimization procedures, we can identify to which subfield each paper most likely belongs. This study can contribute to support scientific policy-makers, funding, and research institutions-via more accurate academic performance evaluations-, to support editors in their tasks to redefine the scopes of journals, and to support popular databases in their processes of refining categories.
    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.11, S.1513-1528
  3. Wu, M.; Liu, Y.-H.; Brownlee, R.; Zhang, X.: Evaluating utility and automatic classification of subject metadata from Research Data Australia (2021) 0.01 
    0.0068072695 = product of:
  0.027229078 = sum of:
    0.027229078 = weight(_text_:research in 453) [ClassicSimilarity], result of:
      0.027229078 = score(doc=453,freq=2.0), product of:
        0.14397179 = queryWeight, product of:
          2.8529835 = idf(docFreq=6931, maxDocs=44218)
          0.050463587 = queryNorm
        0.18912788 = fieldWeight in 453, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.8529835 = idf(docFreq=6931, maxDocs=44218)
          0.046875 = fieldNorm(doc=453)
  0.25 = coord(1/4)
  4. Ahmed, M.; Mukhopadhyay, M.; Mukhopadhyay, P.: Automated knowledge organization : AI ML based subject indexing system for libraries (2023) 0.01 
    0.005672725 = product of:
  0.0226909 = sum of:
    0.0226909 = weight(_text_:research in 977) [ClassicSimilarity], result of:
      0.0226909 = score(doc=977,freq=2.0), product of:
        0.14397179 = queryWeight, product of:
          2.8529835 = idf(docFreq=6931, maxDocs=44218)
          0.050463587 = queryNorm
        0.15760657 = fieldWeight in 977, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.8529835 = idf(docFreq=6931, maxDocs=44218)
          0.0390625 = fieldNorm(doc=977)
  0.25 = coord(1/4)
    Abstract
    The research study as reported here is an attempt to explore the possibilities of an AI/ML-based semi-automated indexing system in a library setup to handle large volumes of documents. It uses the Python virtual environment to install and configure an open source AI environment (named Annif) to feed the LOD (Linked Open Data) dataset of Library of Congress Subject Headings (LCSH) as a standard KOS (Knowledge Organisation System). The framework deployed the Turtle format of LCSH after cleaning the file with Skosify, applied an array of backend algorithms (namely TF-IDF, Omikuji, and NN-Ensemble) to measure relative performance, and selected Snowball as an analyser. The training of Annif was conducted with a large set of bibliographic records populated with subject descriptors (MARC tag 650$a) and indexed by trained LIS professionals. The training dataset is first treated with MarcEdit to export it in a format suitable for OpenRefine, and then in OpenRefine it undergoes many steps to produce a bibliographic record set suitable to train Annif. The framework, after training, has been tested with a bibliographic dataset to measure indexing efficiencies, and finally, the automated indexing framework is integrated with data wrangling software (OpenRefine) to produce suggested headings on a mass scale. The entire framework is based on open-source software, open datasets, and open standards.
  5. Han, K.; Rezapour, R.; Nakamura, K.; Devkota, D.; Miller, D.C.; Diesner, J.: ¬An expert-in-the-loop method for domain-specific document categorization based on small training data (2023) 0.00 
    0.004835752 = product of:
  0.019343007 = sum of:
    0.019343007 = weight(_text_:science in 967) [ClassicSimilarity], result of:
      0.019343007 = score(doc=967,freq=2.0), product of:
        0.1329271 = queryWeight, product of:
          2.6341193 = idf(docFreq=8627, maxDocs=44218)
          0.050463587 = queryNorm
        0.1455159 = fieldWeight in 967, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.6341193 = idf(docFreq=8627, maxDocs=44218)
          0.0390625 = fieldNorm(doc=967)
  0.25 = coord(1/4)
    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.6, S.669-684

Authors

Themes