Search (194 results, page 1 of 10)

  • × theme_ss:"Automatisches Indexieren"
  1. Abdul, H.; Khoo, C.: Automatic indexing of medical literature using phrase matching : an exploratory study 0.19 
    0.19324368 = product of:
  0.38648736 = sum of:
    0.24090096 = weight(_text_:joint in 3669) [ClassicSimilarity], result of:
      0.24090096 = score(doc=3669,freq=2.0), product of:
        0.43664446 = queryWeight, product of:
          6.2418823 = idf(docFreq=234, maxDocs=44421)
          0.06995397 = queryNorm
        0.55170965 = fieldWeight in 3669, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          6.2418823 = idf(docFreq=234, maxDocs=44421)
          0.0625 = fieldNorm(doc=3669)
    0.14558639 = weight(_text_:headings in 3669) [ClassicSimilarity], result of:
      0.14558639 = score(doc=3669,freq=2.0), product of:
        0.33944473 = queryWeight, product of:
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.06995397 = queryNorm
        0.4288957 = fieldWeight in 3669, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.0625 = fieldNorm(doc=3669)
  0.5 = coord(2/4)
    Abstract
    Reports the 1st part of a study to apply the technique of phrase matching to the automatic assignment of MeSH subject headings and subheadings to abstracts of periodical articles.
    Source
    Health information: new directions. Proceedings of the Joint Conference of the Health Libraries Sections of the Australian Library and Information Association and New Zealand Library Association, Auckland, New Zealand, 12.-16.11.1989
  2. Fuhr, N.; Knorz, G.: Retrieval test evaluation of a rule based automatic indexing (AIR/PHYS) (1984) 0.09 
    0.090337865 = product of:
  0.36135146 = sum of:
    0.36135146 = weight(_text_:joint in 2320) [ClassicSimilarity], result of:
      0.36135146 = score(doc=2320,freq=2.0), product of:
        0.43664446 = queryWeight, product of:
          6.2418823 = idf(docFreq=234, maxDocs=44421)
          0.06995397 = queryNorm
        0.8275645 = fieldWeight in 2320, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          6.2418823 = idf(docFreq=234, maxDocs=44421)
          0.09375 = fieldNorm(doc=2320)
  0.25 = coord(1/4)
    Source
    Research and development in information retrieval. Proc. of the 3rd joint BCS and ACM symp., Cambridge, 2.-6.7.1984. Ed.: C.J. van Rijsbergen
  3. Kiros, R.; Salakhutdinov, R.; Zemel, R.S.: Unifying visual-semantic embeddings with multimodal neural language models (2014) 0.06 
    0.063878514 = product of:
  0.25551406 = sum of:
    0.25551406 = weight(_text_:joint in 2871) [ClassicSimilarity], result of:
      0.25551406 = score(doc=2871,freq=4.0), product of:
        0.43664446 = queryWeight, product of:
          6.2418823 = idf(docFreq=234, maxDocs=44421)
          0.06995397 = queryNorm
        0.58517647 = fieldWeight in 2871, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          6.2418823 = idf(docFreq=234, maxDocs=44421)
          0.046875 = fieldNorm(doc=2871)
  0.25 = coord(1/4)
    Abstract
    Inspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space. Our pipeline effectively unifies joint image-text embedding models with multimodal neural language models. We introduce the structure-content neural language model that disentangles the structure of a sentence to its content, conditioned on representations produced by the encoder. The encoder allows one to rank images and sentences while the decoder can generate novel descriptions from scratch. Using LSTM to encode sentences, we match the state-of-the-art performance on Flickr8K and Flickr30K without using object detections. We also set new best results when using the 19-layer Oxford convolutional network. Furthermore we show that with linear encoders, the learned embedding space captures multimodal regularities in terms of vector space arithmetic e.g. *image of a blue car* - "blue" + "red" is near images of red cars. Sample captions generated for 800 images are made available for comparison.
  4. Tavakolizadeh-Ravari, M.: Analysis of the long term dynamics in thesaurus developments and its consequences (2017) 0.06 
    0.060441937 = product of:
  0.120883875 = sum of:
    0.04809068 = weight(_text_:und in 4081) [ClassicSimilarity], result of:
      0.04809068 = score(doc=4081,freq=20.0), product of:
        0.15515085 = queryWeight, product of:
          2.217899 = idf(docFreq=13141, maxDocs=44421)
          0.06995397 = queryNorm
        0.3099608 = fieldWeight in 4081, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          2.217899 = idf(docFreq=13141, maxDocs=44421)
          0.03125 = fieldNorm(doc=4081)
    0.07279319 = weight(_text_:headings in 4081) [ClassicSimilarity], result of:
      0.07279319 = score(doc=4081,freq=2.0), product of:
        0.33944473 = queryWeight, product of:
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.06995397 = queryNorm
        0.21444786 = fieldWeight in 4081, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.03125 = fieldNorm(doc=4081)
  0.5 = coord(2/4)
    Abstract
    Die Arbeit analysiert die dynamische Entwicklung und den Gebrauch von Thesaurusbegriffen. Zusätzlich konzentriert sie sich auf die Faktoren, die die Zahl von Indexbegriffen pro Dokument oder Zeitschrift beeinflussen. Als Untersuchungsobjekt dienten der MeSH und die entsprechende Datenbank "MEDLINE". Die wichtigsten Konsequenzen sind: 1. Der MeSH-Thesaurus hat sich durch drei unterschiedliche Phasen jeweils logarithmisch entwickelt. Solch einen Thesaurus sollte folgenden Gleichung folgen: "T = 3.076,6 Ln (d) - 22.695 + 0,0039d" (T = Begriffe, Ln = natürlicher Logarithmus und d = Dokumente). Um solch einen Thesaurus zu konstruieren, muss man demnach etwa 1.600 Dokumente von unterschiedlichen Themen des Bereiches des Thesaurus haben. Die dynamische Entwicklung von Thesauri wie MeSH erfordert die Einführung eines neuen Begriffs pro Indexierung von 256 neuen Dokumenten. 2. Die Verteilung der Thesaurusbegriffe erbrachte drei Kategorien: starke, normale und selten verwendete Headings. Die letzte Gruppe ist in einer Testphase, während in der ersten und zweiten Kategorie die neu hinzukommenden Deskriptoren zu einem Thesauruswachstum führen. 3. Es gibt ein logarithmisches Verhältnis zwischen der Zahl von Index-Begriffen pro Aufsatz und dessen Seitenzahl für die Artikeln zwischen einer und einundzwanzig Seiten. 4. Zeitschriftenaufsätze, die in MEDLINE mit Abstracts erscheinen erhalten fast zwei Deskriptoren mehr. 5. Die Findablity der nicht-englisch sprachigen Dokumente in MEDLINE ist geringer als die englische Dokumente. 6. Aufsätze der Zeitschriften mit einem Impact Factor 0 bis fünfzehn erhalten nicht mehr Indexbegriffe als die der anderen von MEDINE erfassten Zeitschriften. 7. In einem Indexierungssystem haben unterschiedliche Zeitschriften mehr oder weniger Gewicht in ihrem Findability. Die Verteilung der Indexbegriffe pro Seite hat gezeigt, dass es bei MEDLINE drei Kategorien der Publikationen gibt. Außerdem gibt es wenige stark bevorzugten Zeitschriften."
    Footnote
    Dissertation, Humboldt-Universität zu Berlin - Institut für Bibliotheks- und Informationswissenschaft.
    Imprint
    Berlin : Humboldt-Universität zu Berlin / Institut für Bibliotheks- und Informationswissenschaft
    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  5. Riloff, E.: ¬An empirical study of automated dictionary construction for information extraction in three domains (1996) 0.06 
    0.06022524 = product of:
  0.24090096 = sum of:
    0.24090096 = weight(_text_:joint in 6820) [ClassicSimilarity], result of:
      0.24090096 = score(doc=6820,freq=2.0), product of:
        0.43664446 = queryWeight, product of:
          6.2418823 = idf(docFreq=234, maxDocs=44421)
          0.06995397 = queryNorm
        0.55170965 = fieldWeight in 6820, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          6.2418823 = idf(docFreq=234, maxDocs=44421)
          0.0625 = fieldNorm(doc=6820)
  0.25 = coord(1/4)
    Abstract
    AutoSlog is a system that addresses the knowledge engineering bottleneck for information extraction. AutoSlog automatically creates domain specific dictionaries for information extraction, given an appropriate training corpus. Describes experiments with AutoSlog in terrorism, joint ventures and microelectronics domains. Compares the performance of AutoSlog across the 3 domains, discusses the lessons learned and presents results from 2 experiments which demonstrate that novice users can generate effective dictionaries using AutoSlog
  6. Plaunt, C.; Norgard, B.A.: ¬An association-based method for automatic indexing with a controlled vocabulary (1998) 0.06 
    0.05572068 = product of:
  0.22288272 = sum of:
    0.22288272 = weight(_text_:headings in 2794) [ClassicSimilarity], result of:
      0.22288272 = score(doc=2794,freq=12.0), product of:
        0.33944473 = queryWeight, product of:
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.06995397 = queryNorm
        0.6566098 = fieldWeight in 2794, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.0390625 = fieldNorm(doc=2794)
  0.25 = coord(1/4)
    Abstract
    In this article, we describe and test a two-stage algorithm based on a lexical collocation technique which maps from the lexical clues contained in a document representation into a controlled vocabulary list of subject headings. Using a collection of 4.626 INSPEC documents, we create a 'dictionary' of associations between the lexical items contained in the titles, authors, and abstracts, and controlled vocabulary subject headings assigned to those records by human indexers using a likelihood ratio statistic as the measure of association. In the deployment stage, we use the dictiony to predict which of the controlled vocabulary subject headings best describe new documents when they are presented to the system. Our evaluation of this algorithm, in which we compare the automatically assigned subject headings to the subject headings assigned to the test documents by human catalogers, shows that we can obtain results comparable to, and consistent with, human cataloging. In effect we have cast this as a classic partial match information retrieval problem. We consider the problem to be one of 'retrieving' (or assigning) the most probably 'relevant' (or correct) controlled vocabulary subject headings to a document based on the clues contained in that document
  7. Koch, T.: Experiments with automatic classification of WAIS databases and indexing of WWW : some results from the Nordic WAIS/WWW project (1994) 0.05 
    0.052697085 = product of:
  0.21078834 = sum of:
    0.21078834 = weight(_text_:joint in 7208) [ClassicSimilarity], result of:
      0.21078834 = score(doc=7208,freq=2.0), product of:
        0.43664446 = queryWeight, product of:
          6.2418823 = idf(docFreq=234, maxDocs=44421)
          0.06995397 = queryNorm
        0.48274595 = fieldWeight in 7208, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          6.2418823 = idf(docFreq=234, maxDocs=44421)
          0.0546875 = fieldNorm(doc=7208)
  0.25 = coord(1/4)
    Abstract
    The Nordic WAIS/WWW project sponsored by NORDINFO is a joint project between Lund University Library and the National Technological Library of Denmark. It aims to improve the existing networked information discovery and retrieval tools Wide Area Information System (WAIS) and World Wide Web (WWW), and to move towards unifying WWW and WAIS. Details current results focusing on the WAIS side of the project. Describes research into automatic indexing and classification of WAIS sources, development of an orientation tool for WAIS, and development of a WAIS index of WWW resources
  8. Olsgaard, J.N.; Evans, E.J.: Improving keyword indexing (1981) 0.05 
    0.045495745 = product of:
  0.18198298 = sum of:
    0.18198298 = weight(_text_:headings in 5064) [ClassicSimilarity], result of:
      0.18198298 = score(doc=5064,freq=2.0), product of:
        0.33944473 = queryWeight, product of:
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.06995397 = queryNorm
        0.53611964 = fieldWeight in 5064, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.078125 = fieldNorm(doc=5064)
  0.25 = coord(1/4)
    Abstract
    This communication examines some of the most frequently cited critisms of keyword indexing. These critisms include (1) absence of general subject headings, (2) limited entry points, and (3) irrelevant indexing. Some solutions are suggested to meet these critisms.
  9. Junger, U.: Can indexing be automated? : the example of the Deutsche Nationalbibliothek (2012) 0.05 
    0.04503849 = product of:
  0.18015397 = sum of:
    0.18015397 = weight(_text_:headings in 2717) [ClassicSimilarity], result of:
      0.18015397 = score(doc=2717,freq=4.0), product of:
        0.33944473 = queryWeight, product of:
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.06995397 = queryNorm
        0.5307314 = fieldWeight in 2717, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.0546875 = fieldNorm(doc=2717)
  0.25 = coord(1/4)
    Abstract
    The German subject headings authority file (Schlagwortnormdatei/SWD) provides a broad controlled vocabulary for indexing documents of all subjects. Traditionally used for intellectual subject cataloguing primarily of books the Deutsche Nationalbibliothek (DNB, German National Library) has been working on developping and implementing procedures for automated assignment of subject headings for online publications. This project, its results and problems are sketched in the paper.
  10. Short, M.: Text mining and subject analysis for fiction; or, using machine learning and information extraction to assign subject headings to dime novels (2019) 0.05 
    0.04503849 = product of:
  0.18015397 = sum of:
    0.18015397 = weight(_text_:headings in 481) [ClassicSimilarity], result of:
      0.18015397 = score(doc=481,freq=4.0), product of:
        0.33944473 = queryWeight, product of:
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.06995397 = queryNorm
        0.5307314 = fieldWeight in 481, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.0546875 = fieldNorm(doc=481)
  0.25 = coord(1/4)
    Abstract
    This article describes multiple experiments in text mining at Northern Illinois University that were undertaken to improve the efficiency and accuracy of cataloging. It focuses narrowly on subject analysis of dime novels, a format of inexpensive fiction that was popular in the United States between 1860 and 1915. NIU holds more than 55,000 dime novels in its collections, which it is in the process of comprehensively digitizing. Classification, keyword extraction, named-entity recognition, clustering, and topic modeling are discussed as means of assigning subject headings to improve their discoverability by researchers and to increase the productivity of digitization workflows.
  11. Willis, C.; Losee, R.M.: ¬A random walk on an ontology : using thesaurus structure for automatic subject indexing (2013) 0.04 
    0.044000402 = product of:
  0.088000804 = sum of:
    0.015207609 = weight(_text_:und in 2016) [ClassicSimilarity], result of:
      0.015207609 = score(doc=2016,freq=2.0), product of:
        0.15515085 = queryWeight, product of:
          2.217899 = idf(docFreq=13141, maxDocs=44421)
          0.06995397 = queryNorm
        0.098018214 = fieldWeight in 2016, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.217899 = idf(docFreq=13141, maxDocs=44421)
          0.03125 = fieldNorm(doc=2016)
    0.07279319 = weight(_text_:headings in 2016) [ClassicSimilarity], result of:
      0.07279319 = score(doc=2016,freq=2.0), product of:
        0.33944473 = queryWeight, product of:
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.06995397 = queryNorm
        0.21444786 = fieldWeight in 2016, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.03125 = fieldNorm(doc=2016)
  0.5 = coord(2/4)
    Abstract
    Relationships between terms and features are an essential component of thesauri, ontologies, and a range of controlled vocabularies. In this article, we describe ways to identify important concepts in documents using the relationships in a thesaurus or other vocabulary structures. We introduce a methodology for the analysis and modeling of the indexing process based on a weighted random walk algorithm. The primary goal of this research is the analysis of the contribution of thesaurus structure to the indexing process. The resulting models are evaluated in the context of automatic subject indexing using four collections of documents pre-indexed with 4 different thesauri (AGROVOC [UN Food and Agriculture Organization], high-energy physics taxonomy [HEP], National Agricultural Library Thesaurus [NALT], and medical subject headings [MeSH]). We also introduce a thesaurus-centric matching algorithm intended to improve the quality of candidate concepts. In all cases, the weighted random walk improves automatic indexing performance over matching alone with an increase in average precision (AP) of 9% for HEP, 11% for MeSH, 35% for NALT, and 37% for AGROVOC. The results of the analysis support our hypothesis that subject indexing is in part a browsing process, and that using the vocabulary and its structure in a thesaurus contributes to the indexing process. The amount that the vocabulary structure contributes was found to differ among the 4 thesauri, possibly due to the vocabulary used in the corresponding thesauri and the structural relationships between the terms. Each of the thesauri and the manual indexing associated with it is characterized using the methods developed here.
    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  12. Ma, N.; Zheng, H.T.; Xiao, X.: ¬An ontology-based latent semantic indexing approach using long short-term memory networks (2017) 0.04 
    0.037640776 = product of:
  0.1505631 = sum of:
    0.1505631 = weight(_text_:joint in 4810) [ClassicSimilarity], result of:
      0.1505631 = score(doc=4810,freq=2.0), product of:
        0.43664446 = queryWeight, product of:
          6.2418823 = idf(docFreq=234, maxDocs=44421)
          0.06995397 = queryNorm
        0.34481853 = fieldWeight in 4810, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          6.2418823 = idf(docFreq=234, maxDocs=44421)
          0.0390625 = fieldNorm(doc=4810)
  0.25 = coord(1/4)
    Source
    Web and Big Data: First International Joint Conference, APWeb-WAIM 2017, Beijing, China, July 7-9, 2017, Proceedings, Part I. Eds.: L. Chen et al
  13. Zhang, Y.; Zhang, C.; Li, J.: Joint modeling of characters, words, and conversation contexts for microblog keyphrase extraction (2020) 0.04 
    0.037640776 = product of:
  0.1505631 = sum of:
    0.1505631 = weight(_text_:joint in 816) [ClassicSimilarity], result of:
      0.1505631 = score(doc=816,freq=2.0), product of:
        0.43664446 = queryWeight, product of:
          6.2418823 = idf(docFreq=234, maxDocs=44421)
          0.06995397 = queryNorm
        0.34481853 = fieldWeight in 816, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          6.2418823 = idf(docFreq=234, maxDocs=44421)
          0.0390625 = fieldNorm(doc=816)
  0.25 = coord(1/4)
  14. Losee, R.M.: ¬A Gray code based ordering for documents on shelves : classification for browsing and retrieval (1992) 0.03 
    0.031847022 = product of:
  0.12738809 = sum of:
    0.12738809 = weight(_text_:headings in 2334) [ClassicSimilarity], result of:
      0.12738809 = score(doc=2334,freq=2.0), product of:
        0.33944473 = queryWeight, product of:
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.06995397 = queryNorm
        0.37528375 = fieldWeight in 2334, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.0546875 = fieldNorm(doc=2334)
  0.25 = coord(1/4)
    Abstract
    A document classifier places documents together in a linear arrangement for browsing or high-speed access by human or computerised information retrieval systems. Requirements for document classification and browsing systems are developed from similarity measures, distance measures, and the notion of subject aboutness. A requirement that documents be arranged in decreasing order of similarity as the distance from a given document increases can often not be met. Based on these requirements, information-theoretic considerations, and the Gray code, a classification system is proposed that can classifiy documents without human intervention. A measure of classifier performance is developed, and used to evaluate experimental results comparing the distance between subject headings assigned to documents given classifications from the proposed system and the Library of Congress Classification (LCC) system
  15. Shafer, K.: Scorpion Project explores using Dewey to organize the Web (1996) 0.03 
    0.031847022 = product of:
  0.12738809 = sum of:
    0.12738809 = weight(_text_:headings in 6818) [ClassicSimilarity], result of:
      0.12738809 = score(doc=6818,freq=2.0), product of:
        0.33944473 = queryWeight, product of:
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.06995397 = queryNorm
        0.37528375 = fieldWeight in 6818, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.0546875 = fieldNorm(doc=6818)
  0.25 = coord(1/4)
    Abstract
    As the amount of accessible information on the WWW increases, so will the cost of accessing it, even if search servcies remain free, due to the increasing amount of time users will have to spend to find needed items. Considers what the seemingly unorganized Web and the organized world of libraries can offer each other. The OCLC Scorpion Project is attempting to combine indexing and cataloguing, specifically focusing on building tools for automatic subject recognition using the technqiues of library science and information retrieval. If subject headings or concept domains can be automatically assigned to electronic items, improved filtering tools for searching can be produced
  16. Junger, U.: Can indexing be automated? : the example of the Deutsche Nationalbibliothek (2014) 0.03 
    0.031847022 = product of:
  0.12738809 = sum of:
    0.12738809 = weight(_text_:headings in 2969) [ClassicSimilarity], result of:
      0.12738809 = score(doc=2969,freq=2.0), product of:
        0.33944473 = queryWeight, product of:
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.06995397 = queryNorm
        0.37528375 = fieldWeight in 2969, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.0546875 = fieldNorm(doc=2969)
  0.25 = coord(1/4)
    Abstract
    The German Integrated Authority File (Gemeinsame Normdatei, GND), provides a broad controlled vocabulary for indexing documents on all subjects. Traditionally used for intellectual subject cataloging primarily for books, the Deutsche Nationalbibliothek (DNB, German National Library) has been working on developing and implementing procedures for automated assignment of subject headings for online publications. This project, its results, and problems are outlined in this article.
  17. Moulaison-Sandy, H.; Adkins, D.; Bossaller, J.; Cho, H.: ¬An automated approach to describing fiction : a methodology to use book reviews to identify affect (2021) 0.03 
    0.031847022 = product of:
  0.12738809 = sum of:
    0.12738809 = weight(_text_:headings in 1711) [ClassicSimilarity], result of:
      0.12738809 = score(doc=1711,freq=2.0), product of:
        0.33944473 = queryWeight, product of:
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.06995397 = queryNorm
        0.37528375 = fieldWeight in 1711, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.0546875 = fieldNorm(doc=1711)
  0.25 = coord(1/4)
    Abstract
    Subject headings and genre terms are notoriously difficult to apply, yet are important for fiction. The current project functions as a proof of concept, using a text-mining methodology to identify affective information (emotion and tone) about fiction titles from professional book reviews as a potential first step in automating the subject analysis process. Findings are presented and discussed, comparing results to the range of aboutness and isness information in library cataloging records. The methodology is likewise presented, and how future work might expand on the current project to enhance catalog records through text-mining is explored.
  18. Chou, C.; Chu, T.: ¬An analysis of BERT (NLP) for assisted subject indexing for Project Gutenberg (2022) 0.03 
    0.031847022 = product of:
  0.12738809 = sum of:
    0.12738809 = weight(_text_:headings in 2141) [ClassicSimilarity], result of:
      0.12738809 = score(doc=2141,freq=2.0), product of:
        0.33944473 = queryWeight, product of:
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.06995397 = queryNorm
        0.37528375 = fieldWeight in 2141, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.0546875 = fieldNorm(doc=2141)
  0.25 = coord(1/4)
    Abstract
    In light of AI (Artificial Intelligence) and NLP (Natural language processing) technologies, this article examines the feasibility of using AI/NLP models to enhance the subject indexing of digital resources. While BERT (Bidirectional Encoder Representations from Transformers) models are widely used in scholarly communities, the authors assess whether BERT models can be used in machine-assisted indexing in the Project Gutenberg collection, through suggesting Library of Congress subject headings filtered by certain Library of Congress Classification subclass labels. The findings of this study are informative for further research on BERT models to assist with automatic subject indexing for digital library collections.
  19. Gil-Leiva, I.: SISA-automatic indexing system for scientific articles : experiments with location heuristics rules versus TF-IDF rules (2017) 0.03 
    0.027297448 = product of:
  0.10918979 = sum of:
    0.10918979 = weight(_text_:headings in 4622) [ClassicSimilarity], result of:
      0.10918979 = score(doc=4622,freq=2.0), product of:
        0.33944473 = queryWeight, product of:
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.06995397 = queryNorm
        0.32167178 = fieldWeight in 4622, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.8524013 = idf(docFreq=942, maxDocs=44421)
          0.046875 = fieldNorm(doc=4622)
  0.25 = coord(1/4)
    Abstract
    Indexing is contextualized and a brief description is provided of some of the most used automatic indexing systems. We describe SISA, a system which uses location heuristics rules, statistical rules like term frequency (TF) or TF-IDF to obtain automatic or semi-automatic indexing, depending on the user's preference. The aim of this research is to ascertain which rules (location heuristics rules or TF-IDF rules) provide the best indexing terms. SISA is used to obtain the automatic indexing of 200 scientific articles on fruit growing written in Portuguese. It uses, on the one hand, location heuristics rules founded on the value of certain parts of the articles for indexing such as titles, abstracts, keywords, headings, first paragraph, conclusions and references and, on the other, TF-IDF rules. The indexing is then evaluated to ascertain retrieval performance through recall, precision and f-measure. Automatic indexing of the articles with location heuristics rules provided the best results with the evaluation measures.
  20. Thönssen, B.: Automatische Indexierung und Schnittstellen zu Thesauri (1988) 0.02 
    0.023281801 = product of:
  0.093127206 = sum of:
    0.093127206 = weight(_text_:und in 29) [ClassicSimilarity], result of:
      0.093127206 = score(doc=29,freq=12.0), product of:
        0.15515085 = queryWeight, product of:
          2.217899 = idf(docFreq=13141, maxDocs=44421)
          0.06995397 = queryNorm
        0.60023654 = fieldWeight in 29, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          2.217899 = idf(docFreq=13141, maxDocs=44421)
          0.078125 = fieldNorm(doc=29)
  0.25 = coord(1/4)
    Abstract
    Über eine Schnittstelle zwischen Programmen zur automatischen Indexierung (PRIMUS-IDX) und zur maschinellen Thesaurusverwaltung (INDEX) sollen große Textmengen schnell, kostengünstig und konsistent erschlossen und verbesserte Recherchemöglichkeiten geschaffen werden. Zielvorstellung ist ein Verfahren, das auf PCs ablauffähig ist und speziell deutschsprachige Texte bearbeiten kann
    Theme
    Konzeption und Anwendung des Prinzips Thesaurus

Authors

Years

Languages

  • d 162
  • e 30
  • a 1
  • m 1
  • nl 1
  • More… Less…

Types

  • a 142
  • x 31
  • el 24
  • m 11
  • s 5
  • d 1
  • h 1
  • p 1
  • r 1
  • More… Less…

Themes

Subjects

Classifications