-
Anderson, J.D.; Pérez-Carballo, J.: ¬The nature of indexing: how humans and machines analyze messages and texts for retrieval : Part II: Machine indexing, and the allocation of human versus machine effort (2001)
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-
Pulgarin, A.; Gil-Leiva, I.: Bibliometric analysis of the automatic indexing literature : 1956-2000 (2004)
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- Abstract
- We present a bibliometric study of a corpus of 839 bibliographic references about automatic indexing, covering the period 1956-2000. We analyse the distribution of authors and works, the obsolescence and its dispersion, and the distribution of the literature by topic, year, and source type. We conclude that: (i) there has been a constant interest on the part of researchers; (ii) the most studied topics were the techniques and methods employed and the general aspects of automatic indexing; (iii) the productivity of the authors does fit a Lotka distribution (Dmax=0.02 and critical value=0.054); (iv) the annual aging factor is 95%; and (v) the dispersion of the literature is low.
-
Munkelt, J.: Erstellung einer DNB-Retrieval-Testkollektion (2018)
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- Pages
- II, 79 S
-
Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986)
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- Source
- Information processing and management. 22(1986) no.6, S.465-476
-
Flores, F.N.; Moreira, V.P.: Assessing the impact of stemming accuracy on information retrieval : a multilingual perspective (2016)
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- Abstract
- The quality of stemming algorithms is typically measured in two different ways: (i) how accurately they map the variant forms of a word to the same stem; or (ii) how much improvement they bring to Information Retrieval systems. In this article, we evaluate various stemming algorithms, in four languages, in terms of accuracy and in terms of their aid to Information Retrieval. The aim is to assess whether the most accurate stemmers are also the ones that bring the biggest gain in Information Retrieval. Experiments in English, French, Portuguese, and Spanish show that this is not always the case, as stemmers with higher error rates yield better retrieval quality. As a byproduct, we also identified the most accurate stemmers and the best for Information Retrieval purposes.
-
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
-
Ahmed, M.: Automatic indexing for agriculture : designing a framework by deploying Agrovoc, Agris and Annif (2023)
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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).
-
Biebricher, N.; Fuhr, N.; Lustig, G.; Schwantner, M.; Knorz, G.: ¬The automatic indexing system AIR/PHYS : from research to application (1988)
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- Date
- 16. 8.1998 12:51:22
-
Kutschekmanesch, S.; Lutes, B.; Moelle, K.; Thiel, U.; Tzeras, K.: Automated multilingual indexing : a synthesis of rule-based and thesaurus-based methods (1998)
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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
-
Stankovic, R. et al.: Indexing of textual databases based on lexical resources : a case study for Serbian (2016)
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- Date
- 1. 2.2016 18:25:22
-
Tsujii, J.-I.: Automatic acquisition of semantic collocation from corpora (1995)
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- Date
- 31. 7.1996 9:22:19
-
Riloff, E.: ¬An empirical study of automated dictionary construction for information extraction in three domains (1996)
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- Date
- 6. 3.1997 16:22:15
-
Hodges, P.R.: Keyword in title indexes : effectiveness of retrieval in computer searches (1983)
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- Date
- 14. 3.1996 13:22:21
-
Bordoni, L.; Pazienza, M.T.: Documents automatic indexing in an environmental domain (1997)
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- Source
- International forum on information and documentation. 22(1997) no.1, S.17-28
-
Wolfekuhler, M.R.; Punch, W.F.: Finding salient features for personal Web pages categories (1997)
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- Date
- 1. 8.1996 22:08:06
-
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
-
Ward, M.L.: ¬The future of the human indexer (1996)
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- Date
- 9. 2.1997 18:44:22
-
Plaunt, C.; Norgard, B.A.: ¬An association-based method for automatic indexing with a controlled vocabulary (1998)
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- Date
- 11. 9.2000 19:53:22
-
Milstead, J.L.: Thesauri in a full-text world (1998)
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- Date
- 22. 9.1997 19:16:05
-
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)
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- 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
