AUTOMATIC SPECIALIZED VS. NON-SPECIALIZED TEXTS DIFFERENTIATION: A FIRST APPROACH - Archive ouverte HAL Access content directly
Conference Papers Year : 2010

AUTOMATIC SPECIALIZED VS. NON-SPECIALIZED TEXTS DIFFERENTIATION: A FIRST APPROACH

Abstract

In this paper we would like to show that certain grammatical features, besides lexicon, have a strong potential to differentiate specialized texts from non-specialized texts. We have developed a tool including these features and it has been trained using machine learning techniques based on association rules using two sub-corpora (specialized vs. non-specialized), each one divided into training and test corpora. We have evaluated this tool and the results show that the strategy we have used is suitable to differentiate specialized texts from plain texts. These results could be considered as an innovative perspective to research on domains related with terminology, specialized discourse and computational linguistics, with applications to automatic compilation of Languages for Specific Purposes (LSP) corpora and optimization of search engines among others.
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Dates and versions

hal-02556652 , version 1 (28-04-2020)

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  • HAL Id : hal-02556652 , version 1

Cite

M. Teresa Cabre, Iria da Cunha, Eric Sanjuan, Juan-Manuel Torres-Moreno, Jorge Vivaldi. AUTOMATIC SPECIALIZED VS. NON-SPECIALIZED TEXTS DIFFERENTIATION: A FIRST APPROACH. TECHNOLOGICAL INNOVATION IN THE TEACHING AND PROCESSING OF LSP, Apr 2010, Madrid, Spain. ⟨hal-02556652⟩

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UNIV-AVIGNON LIA
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