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  • × theme_ss:"Automatisches Indexieren"
  1. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.01 
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  2. Leung, C.-H.; Kan, W.-K.: ¬A statistical learning approach to automatic indexing of controlled index terms (1997) 0.01 
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    Abstract
    A statistical learning approach to assigning controlled index terms is presented. In this approach, there are two processes: (1) the learning process and (2) the indexing process. The learning process constructs a relationship between an index term and the words relevant and irrelevant to it, based on the positive training set and negative training set, and those not indexed by it, respectively. The indexing process determines whether an index term is assigned to a certain document, based on the relationship constructed by the learning process, and the text found in the document. Furthermore, a learning feedback technique is introduced. This technique used in the learning process modifies the relationship between an index term and its relevant and irrelevant words to improve the learning performance and, thus, the indexing performance. Experimental results have shown that the statistical learning approach and the learning feedback technique are practical means to automatic indexing of controlled index terms
  3. Martins, A.L.; Souza, R.R.; Ribeiro de Mello, H.: ¬The use of noun phrases in information retrieval : proposing a mechanism for automatic classification (2014) 0.01 
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    Abstract
    This paper presents a research on syntactic structures known as noun phrases (NP) being applied to increase the effectiveness and efficiency of the mechanisms for the document's classification. Our hypothesis is the fact that the NP can be used instead of single words as a semantic aggregator to reduce the number of words that will be used for the classification system without losing its semantic coverage, increasing its efficiency. The experiment divided the documents classification process in three phases: a) NP preprocessing b) system training; and c) classification experiments. In the first step, a corpus of digitalized texts was submitted to a natural language processing platform1 in which the part-of-speech tagging was done, and them PERL scripts pertaining to the PALAVRAS package were used to extract the Noun Phrases. The preprocessing also involved the tasks of a) removing NP low meaning pre-modifiers, as quantifiers; b) identification of synonyms and corresponding substitution for common hyperonyms; and c) stemming of the relevant words contained in the NP, for similitude checking with other NPs. The first tests with the resulting documents have demonstrated its effectiveness. We have compared the structural similarity of the documents before and after the whole pre-processing steps of phase one. The texts maintained the consistency with the original and have kept the readability. The second phase involves submitting the modified documents to a SVM algorithm to identify clusters and classify the documents. The classification rules are to be established using a machine learning approach. Finally, tests will be conducted to check the effectiveness of the whole process.
    Source
    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
  4. Fuhr, N.; Niewelt, B.: ¬Ein Retrievaltest mit automatisch indexierten Dokumenten (1984) 0.01 
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    Date
    20.10.2000 12:22:23
  5. Hlava, M.M.K.: Automatic indexing : comparing rule-based and statistics-based indexing systems (2005) 0.01 
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    Source
    Information outlook. 9(2005) no.8, S.22-23
  6. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.01 
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    Date
    14. 6.2015 22:12:44
  7. Hauer, M.: Automatische Indexierung (2000) 0.01 
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    Source
    Wissen in Aktion: Wege des Knowledge Managements. 22. Online-Tagung der DGI, Frankfurt am Main, 2.-4.5.2000. Proceedings. Hrsg.: R. Schmidt
  8. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.01 
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    Date
    14. 6.2015 22:12:56
  9. Hauer, M.: Tiefenindexierung im Bibliothekskatalog : 17 Jahre intelligentCAPTURE (2019) 0.01 
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    Source
    B.I.T.online. 22(2019) H.2, S.163-166
  10. Biebricher, N.; Fuhr, N.; Lustig, G.; Schwantner, M.; Knorz, G.: ¬The automatic indexing system AIR/PHYS : from research to application (1988) 0.01 
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    Date
    16. 8.1998 12:51:22
  11. Kutschekmanesch, S.; Lutes, B.; Moelle, K.; Thiel, U.; Tzeras, K.: Automated multilingual indexing : a synthesis of rule-based and thesaurus-based methods (1998) 0.01 
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    Source
    Information und Märkte: 50. Deutscher Dokumentartag 1998, Kongreß der Deutschen Gesellschaft für Dokumentation e.V. (DGD), Rheinische Friedrich-Wilhelms-Universität Bonn, 22.-24. September 1998. Hrsg. von Marlies Ockenfeld u. Gerhard J. Mantwill
  12. Tsareva, P.V.: Algoritmy dlya raspoznavaniya pozitivnykh i negativnykh vkhozdenii deskriptorov v tekst i protsedura avtomaticheskoi klassifikatsii tekstov (1999) 0.01 
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    Date
    1. 4.2002 10:22:41
  13. Stankovic, R. et al.: Indexing of textual databases based on lexical resources : a case study for Serbian (2016) 0.01 
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    Date
    1. 2.2016 18:25:22
  14. Selisskaya, M.A.: Ispol'zovanie mashinnogo obucheniya pri avtomaticheskoi klassifikatsii tekstov (1999) 0.01 
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    Footnote
    Übers. des Titels: Machine learning as a tool for development of automated text indexing systems
  15. Tsujii, J.-I.: Automatic acquisition of semantic collocation from corpora (1995) 0.00 
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    Date
    31. 7.1996 9:22:19
  16. Riloff, E.: ¬An empirical study of automated dictionary construction for information extraction in three domains (1996) 0.00 
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    Date
    6. 3.1997 16:22:15
  17. Lepsky, K.; Vorhauer, J.: Lingo - ein open source System für die Automatische Indexierung deutschsprachiger Dokumente (2006) 0.00 
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    Date
    24. 3.2006 12:22:02
  18. Probst, M.; Mittelbach, J.: Maschinelle Indexierung in der Sacherschließung wissenschaftlicher Bibliotheken (2006) 0.00 
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    Date
    22. 3.2008 12:35:19
  19. Glaesener, L.: Automatisches Indexieren einer informationswissenschaftlichen Datenbank mit Mehrwortgruppen (2012) 0.00 
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    Date
    11. 9.2012 19:43:22
  20. Ahmed, M.: Automatic indexing for agriculture : designing a framework by deploying Agrovoc, Agris and Annif (2023) 0.00 
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    Abstract
    There are several ways to employ machine learning for automating subject indexing. One popular strategy is to utilize a supervised learning algorithm to train a model on a set of documents that have been manually indexed by subject matter using a standard vocabulary. The resulting model can then predict the subject of new and previously unseen documents by identifying patterns learned from the training data. To do this, the first step is to gather a large dataset of documents and manually assign each document a set of subject keywords/descriptors from a controlled vocabulary (e.g., from Agrovoc). Next, the dataset (obtained from Agris) can be divided into - i) a training dataset, and ii) a test dataset. The training dataset is used to train the model, while the test dataset is used to evaluate the model's performance. Machine learning can be a powerful tool for automating the process of subject indexing. This research is an attempt to apply Annif (http://annif. org/), an open-source AI/ML framework, to autogenerate subject keywords/descriptors for documentary resources in the domain of agriculture. The training dataset is obtained from Agris, which applies the Agrovoc thesaurus as a vocabulary tool (https://www.fao.org/agris/download).

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