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Extraction of the Useful Words from a Decisional Corpus. Contribution of Correspondence Analysis.

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Title: Extraction of the Useful Words from a Decisional Corpus. Contribution of Correspondence Analysis.
Authors: Kacprzyk, Janusz, Sirmakessis, Spiros, Bécue-Bertaut, Mónica, Rajman, Martin, Lebart, Ludovic, Gaussier, Eric
Source: Knowledge Mining; 2005, p159-179, 21p
Subject Terms: Correspondence analysis (Statistics), Multivariate analysis, MAP (Computer program language), FOCUS (Computer program language), Case studies
Abstract: In the framework of the JuriSent case study, carried out within the European NEMIS thematic network, we analyze the contribution of text mining techniques to improve the consultation of jurisprudence textual databases. We mainly focus on correspondence analysis (CA) techniques, but also provide some insights on similar visualization techniques, such as self organizing maps (Kohonen maps), and review the potential impact of various Natural Language pre-processing techniques. CA is described in more detail, as well as its use in all the steps of the analysis. A concrete example is provided to illustrate the value of the results obtained with CA techniques for an enhanced access to the studied jurisprudence corpus. [ABSTRACT FROM AUTHOR]
Copyright of Knowledge Mining is the property of Springer Nature / Books and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
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An: 26190628
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PubType: Book
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  Data: Extraction of the Useful Words from a Decisional Corpus. Contribution of Correspondence Analysis.
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  Data: <searchLink fieldCode="AR" term="%22Kacprzyk%2C+Janusz%22">Kacprzyk, Janusz</searchLink><br /><searchLink fieldCode="AR" term="%22Sirmakessis%2C+Spiros%22">Sirmakessis, Spiros</searchLink><br /><searchLink fieldCode="AR" term="%22Bécue-Bertaut%2C+Mónica%22">Bécue-Bertaut, Mónica</searchLink><br /><searchLink fieldCode="AR" term="%22Rajman%2C+Martin%22">Rajman, Martin</searchLink><br /><searchLink fieldCode="AR" term="%22Lebart%2C+Ludovic%22">Lebart, Ludovic</searchLink><br /><searchLink fieldCode="AR" term="%22Gaussier%2C+Eric%22">Gaussier, Eric</searchLink>
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  Data: Knowledge Mining; 2005, p159-179, 21p
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  Data: <searchLink fieldCode="DE" term="%22Correspondence+analysis+%28Statistics%29%22">Correspondence analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Multivariate+analysis%22">Multivariate analysis</searchLink><br /><searchLink fieldCode="DE" term="%22MAP+%28Computer+program+language%29%22">MAP (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22FOCUS+%28Computer+program+language%29%22">FOCUS (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Case+studies%22">Case studies</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In the framework of the JuriSent case study, carried out within the European NEMIS thematic network, we analyze the contribution of text mining techniques to improve the consultation of jurisprudence textual databases. We mainly focus on correspondence analysis (CA) techniques, but also provide some insights on similar visualization techniques, such as self organizing maps (Kohonen maps), and review the potential impact of various Natural Language pre-processing techniques. CA is described in more detail, as well as its use in all the steps of the analysis. A concrete example is provided to illustrate the value of the results obtained with CA techniques for an enhanced access to the studied jurisprudence corpus. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Knowledge Mining is the property of Springer Nature / Books and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/3-540-32394-5•12
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        Text: English
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              Text: 2005
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