TY  - Type of reference 
TI  - Keyword Representations in Semantic Vector Space: a Keyword Assignment Method for Automatic Document Indexing 
AU  - Jean-François Chartier 
AU  - Dominic Forest  
AB  - With the extremely rapid growth of the amount of digital documents in our societies, automatic keyword
indexing has become a central research issue in information retrieval and document management. Several scientific
competitions dealing with automatic indexing tasks have emerged in recent years. This article reports our participation in
one of them, the 2016 edition of Défi Fouille de Texte (DEFT-2016). First, we present a state of the art regarding the
importance, the issues and the challenges of automatic keyword indexing. After presenting the context and the task of the
DEFT-2016, we introduce the method we have developed. This method is based on the construction of a keyword
semantic vector space. The evaluation of our method and the analysis of the results suggest that our approach is
particularly adapted to automatic keyword indexing tasks which require a large proportion of controlled keyword
assignment that are absent from the text content of the documents. 
DO  - 10.21494/ISTE.OP.2018.0206 
JF  - Information Retrieval, Document and Semantic Web 
KW  - Automatic Keyword Indexing,  Keyword Assignment,  Keyword Extraction,  Supervised Machine Learning, 
Unsupervised Machine Learning,  Semantic Vector Space,  Défi Fouille de Textes,  DEFT,  extraction de mots-clés,  assignation de mots-clés, indexation automatique,  Algorithme non-supervisé, 
Algorithme supervisé,  Espace sémantique,  Défi fouille de textes,  DEFT,  
L1  - https://openscience.fr/IMG/pdf/iste_ridows17v1n5.pdf 
LA  - en 
PB  - ISTE OpenScience 
DA  - 2018/01/26 
SN  - 2516-3280 
TT  - Les espaces sémantiques de mots-clés : une méthode d’indexation automatique de documents par assignation de mots-clés 
UR  - https://openscience.fr/Keyword-Representations-in-Semantic-Vector-Space-a-Keyword-Assignment-Method 
IS  - Issue 1 
VL  - 1 
ER  -