Search (39 results, page 1 of 2)

Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.02

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Source: Information processing and management. 22(1986) no.6, S.465-476

Hlava, M.M.K.: Automatic indexing : comparing rule-based and statistics-based indexing systems (2005) 0.02

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Source: Information outlook. 9(2005) no.8, S.22-23

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

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

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

Tsujii, J.-I.: Automatic acquisition of semantic collocation from corpora (1995) 0.01

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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) 0.01

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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) 0.01

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Date: 14. 3.1996 13:22:21

Bordoni, L.; Pazienza, M.T.: Documents automatic indexing in an environmental domain (1997) 0.01

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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) 0.01

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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) 0.01

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Date: 22. 7.2006 17:32:00

Ward, M.L.: ¬The future of the human indexer (1996) 0.01

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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) 0.01

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Date: 11. 9.2000 19:53:22

Milstead, J.L.: Thesauri in a full-text world (1998) 0.01

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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) 0.01

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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

Mesquita, L.A.P.; Souza, R.R.; Baracho Porto, R.M.A.: Noun phrases in automatic indexing: : a structural analysis of the distribution of relevant terms in doctoral theses (2014) 0.01

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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

Greiner-Petter, A.; Schubotz, M.; Cohl, H.S.; Gipp, B.: Semantic preserving bijective mappings for expressions involving special functions between computer algebra systems and document preparation systems (2019) 0.01

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Date: 20. 1.2015 18:30:22

Golub, K.; Soergel, D.; Buchanan, G.; Tudhope, D.; Lykke, M.; Hiom, D.: ¬A framework for evaluating automatic indexing or classification in the context of retrieval (2016) 0.00

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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) 0.00

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Source: D-Lib magazine. 9(2003) no.12, x S

Cui, H.; Boufford, D.; Selden, P.: Semantic annotation of biosystematics literature without training examples (2010) 0.00
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```
Abstract

This article presents an unsupervised algorithm for semantic annotation of morphological descriptions of whole organisms. The algorithm is able to annotate plain text descriptions with high accuracy at the clause level by exploiting the corpus itself. In other words, the algorithm does not need lexicons, syntactic parsers, training examples, or annotation templates. The evaluation on two real-life description collections in botany and paleontology shows that the algorithm has the following desirable features: (a) reduces/eliminates manual labor required to compile dictionaries and prepare source documents; (b) improves annotation coverage: the algorithm annotates what appears in documents and is not limited by predefined and often incomplete templates; (c) learns clean and reusable concepts: the algorithm learns organ names and character states that can be used to construct reusable domain lexicons, as opposed to collection-dependent patterns whose applicability is often limited to a particular collection; (d) insensitive to collection size; and (e) runs in linear time with respect to the number of clauses to be annotated.

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