Search (148 results, page 1 of 8)

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Date

26. 5.1997 21:16:19

Source

Computational linguistics. 22(1996) no.2, S.217-248

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Date

22. 9.1997 19:16:05
6.12.1998 11:26:27

Source

Visualizing subject access for 21st century information resources: Papers presented at the 1997 Clinic on Library Applications of Data Processing, 2-4 Mar 1997, Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign. Ed.: P.A. Cochrane et al

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Content

Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.

Date

8. 1.2013 10:22:32

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Abstract

A technology is described which supports the acquisition, visualisation and manipulation of large vocabularies with associated structures. It is used for dictionary production, terminology data bases, thesauri, library classification systems etc. Essential features of the technology are a lexicographic user interface, variable word description, unlimited list of word readings, a concept language, automatic transformations of formulas into graphic structures, structure manipulation operations and retransformation into formulas. The concept language includes notations for undefined concepts. The structure of defined concepts can be constructed interactively. The technology supports the generation of large vocabularies with structures representing word senses. Concept structures and ordering systems for indexing and retrieval can be constructed separately and connected by associating relations.

Date

30.12.2001 19:01:22

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Date

26. 7.2002 14:02:31

Footnote

Bereits 1991 bei Kluwer publiziert // Rez. in: Knowledge organization 22(1995) no.1, S.48-49 (O. Sechser)

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Abstract

Information retrieval - the retrieval, primarily, of documents or textual material - is fundamentally a linguistic process. At the very least we must describe what we want and match that description with descriptions of the information that is available to us. Furthermore, when we describe what we want, we must mean something by that description. This is a deceptively simple act, but such linguistic events have been the grist for philosophical analysis since Aristotle. Although there are complexities involved in referring to authors, document types, or other categories of information retrieval context, here I wish to focus an one of the most problematic activities in information retrieval: the description of the intellectual content of information items. And even though I take information retrieval to involve the description and retrieval of written text, what I say here is applicable to any information item whose intellectual content can be described for retrieval-books, documents, images, audio clips, video clips, scientific specimens, engineering schematics, and so forth. For convenience, though, I will refer only to the description and retrieval of documents. The description of intellectual content can go wrong in many obvious ways. We may describe what we want incorrectly; we may describe it correctly but in such general terms that its description is useless for retrieval; or we may describe what we want correctly, but misinterpret the descriptions of available information, and thereby match our description of what we want incorrectly. From a linguistic point of view, we can be misunderstood in the process of retrieval in many ways. Because the philosophy of language deals specifically with how we are understood and mis-understood, it should have some use for understanding the process of description in information retrieval. First, however, let us examine more closely the kinds of misunderstandings that can occur in information retrieval. We use language in searching for information in two principal ways. We use it to describe what we want and to discriminate what we want from other information that is available to us but that we do not want. Description and discrimination together articulate the goals of the information search process; they also delineate the two principal ways in which language can fail us in this process. Van Rijsbergen (1979) was the first to make this distinction, calling them "representation" and "discrimination.""

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Date

26. 4.2000 18:48:07

Source

OCLC newsletter. 1998, no.234, Jul/Aug, S.22-24

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Date

17. 1.1997 17:19:26
10.12.2000 18:22:35

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Abstract

Research limitations/implications In total, 42 definitions were identified spanning a period of 11 years. This represented the first use of KM through the estimated apex of terms used. From 2006 onwards definitions were used in repetition, and all definitions that were considered to repeat were therefore subsequently excluded as not being unique instances. All definitions listed are by no means complete and exhaustive. The definitions are viewed outside the scope and context in which they were originally formulated and then used to review the key concepts in the definitions themselves. Social implications When the authors refer to the aforementioned discussion of KM content as well as the presentation of the method followed in this paper, the authors may have a few implications for future research in KM. First the research validates ideas presented by the OECD in 2005 pertaining to KM. It also validates that through the evolution of KM, the authors ended with a description of KM that may be seen as a standardised description. If the authors as academics and practitioners, for example, refer to KM as the same construct and/or idea, it has the potential to speculatively, distinguish between what KM may or may not be. Originality/value By simplifying the term used to define KM, by focusing on the most common definitions, the paper assist in refocusing KM by reconsidering the dimensions that is the most common in how it has been defined over time. This would hopefully assist in reigniting discussions about KM and how it may be used to the benefit of an organisation.

Date

20. 1.2015 18:30:22

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Abstract

This paper will survey the capacity of natural language processing (NLP) systems to identify terms or concept names related to a specific field of knowledge (construction of a reference terminology) and the logico-semantic relations they entertain. The scope of our study will be limited to French language NLP systems whose purpose is automatic terms identification with textual area-grounded terms providing access keys to information

Source

TKE'96: Terminology and knowledge engineering. Proceedings 4th International Congress on Terminology and Knowledge Engineering, 26.-28.8.1996, Wien. Ed.: C. Galinski u. K.-D. Schmitz

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Date

26. 7.1996 19:42:42
26. 3.2002 10:54:54

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Date

17.10.1996 10:26:01

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Source

Information processing and management. 26(1990) no.1, S.73-92

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Source

Information processing and management. 26(1990), S.615-628

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Date

26. 2.1997 11:03:13

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Date

1. 4.2002 10:26:34

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Source

Journal of information science. 26(2000) no.2, S.75-85

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Date

5. 1.1996 10:26:12

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Abstract

Presents a description system for the transformation of structured documents based on Context Free Grammars (CFGs). The system caters to transformations between different document class descriptions, and is presented mainly in Terms of logical structure transformation. Proposes 2 requirements for transformation; the output document class must be explicitly represented; and inconsistency must be avoided. Introducs a grammar for document class descriptions; tree-preserving Context Free Grammar, and gives Syntax-Directed Tree Translation (SDTT) for transformations of a document. The SDTT transformation is formal, concise and consistent with the above 2 requirements

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Abstract

Considers language processing techniques necessary for the implementation of a document retrieval system for Turkish text databases. Introduces the main characteristics of the Turkish language. Discusses the development of a stopword list and the evaluation of a stemming algorithm that takes account of the language's morphological structure. A 2 level description of Turkish morphology developed in Bilkent University, Ankara, is incorporated into a morphological parser, PC-KIMMO, to carry out stemming in Turkish databases. Describes the evaluation of string similarity measures - n-gram matching techniques - for Turkish. Reports experiments on 6 different Turkish text corpora