-
Hlava, M.M.K.: Automatic indexing : comparing rule-based and statistics-based indexing systems (2005)
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- Source
- Information outlook. 9(2005) no.8, S.22-23
-
Hauer, M.: Automatische Indexierung (2000)
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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
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Roberts, D.; Souter, C.: ¬The automation of controlled vocabulary subject indexing of medical journal articles (2000)
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- Abstract
- This article discusses the possibility of the automation of sophisticated subject indexing of medical journal articles. Approaches to subject descriptor assignment in information retrieval research are usually either based upon the manual descriptors in the database or generation of search parameters from the text of the article. The principles of the Medline indexing system are described, followed by a summary of a pilot project, based upon the Amed database. The results suggest that a more extended study, based upon Medline, should encompass various components: Extraction of 'concept strings' from titles and abstracts of records, based upon linguistic features characteristic of medical literature. Use of the Unified Medical Language System (UMLS) for identification of controlled vocabulary descriptors. Coordination of descriptors, utilising features of the Medline indexing system. The emphasis should be on system manipulation of data, based upon input, available resources and specifically designed rules.
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Lepsky, K.; Vorhauer, J.: Lingo - ein open source System für die Automatische Indexierung deutschsprachiger Dokumente (2006)
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- Date
- 24. 3.2006 12:22:02
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Probst, M.; Mittelbach, J.: Maschinelle Indexierung in der Sacherschließung wissenschaftlicher Bibliotheken (2006)
0.01
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- Date
- 22. 3.2008 12:35:19
-
Renz, M.: Automatische Inhaltserschließung im Zeichen von Wissensmanagement (2001)
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- Date
- 22. 3.2001 13:14:48
-
Newman, D.J.; Block, S.: Probabilistic topic decomposition of an eighteenth-century American newspaper (2006)
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- Date
- 22. 7.2006 17:32:00
-
Souza, R.R.; Raghavan, K.S.: ¬A methodology for noun phrase-based automatic indexing (2006)
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- Abstract
- The scholarly community is increasingly employing the Web both for publication of scholarly output and for locating and accessing relevant scholarly literature. Organization of this vast body of digital information assumes significance in this context. The sheer volume of digital information to be handled makes traditional indexing and knowledge representation strategies ineffective and impractical. It is, therefore, worth exploring new approaches. An approach being discussed considers the intrinsic semantics of texts of documents. Based on the hypothesis that noun phrases in a text are semantically rich in terms of their ability to represent the subject content of the document, this approach seeks to identify and extract noun phrases instead of single keywords, and use them as descriptors. This paper presents a methodology that has been developed for extracting noun phrases from Portuguese texts. The results of an experiment carried out to test the adequacy of the methodology are also presented.
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Mongin, L.; Fu, Y.Y.; Mostafa, J.: Open Archives data Service prototype and automated subject indexing using D-Lib archive content as a testbed (2003)
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-
Lorenz, S.: Konzeption und prototypische Realisierung einer begriffsbasierten Texterschließung (2006)
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- Date
- 22. 3.2015 9:17:30
-
Humphrey, S.M.; Névéol, A.; Browne, A.; Gobeil, J.; Ruch, P.; Darmoni, S.J.: Comparing a rule-based versus statistical system for automatic categorization of MEDLINE documents according to biomedical specialty (2009)
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- Abstract
- Automatic document categorization is an important research problem in Information Science and Natural Language Processing. Many applications, including, Word Sense Disambiguation and Information Retrieval in large collections, can benefit from such categorization. This paper focuses on automatic categorization of documents from the biomedical literature into broad discipline-based categories. Two different systems are described and contrasted: CISMeF, which uses rules based on human indexing of the documents by the Medical Subject Headings (MeSH) controlled vocabulary in order to assign metaterms (MTs), and Journal Descriptor Indexing (JDI), based on human categorization of about 4,000 journals and statistical associations between journal descriptors (JDs) and textwords in the documents. We evaluate and compare the performance of these systems against a gold standard of humanly assigned categories for 100 MEDLINE documents, using six measures selected from trec_eval. The results show that for five of the measures performance is comparable, and for one measure JDI is superior. We conclude that these results favor JDI, given the significantly greater intellectual overhead involved in human indexing and maintaining a rule base for mapping MeSH terms to MTs. We also note a JDI method that associates JDs with MeSH indexing rather than textwords, and it may be worthwhile to investigate whether this JDI method (statistical) and CISMeF (rule-based) might be combined and then evaluated showing they are complementary to one another.
-
Nohr, H.: Grundlagen der automatischen Indexierung : ein Lehrbuch (2003)
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- Date
- 22. 6.2009 12:46:51
