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Título
Information retrieval methodology for aiding scienti c database search
Autor(es)
Palabras clave
Information processing
Information retrieval
Systematic literature review
Information technology
Text mining
Vector Space Model
Support Vector Machine
Fecha de publicación
2018
Citación
Marcos-Pablos, S., & García-Peñalvo, F. J. (2019). Information retrieval methodology for aiding scientific database search. Soft Computing, doi:10.1007/s00500-018-3568-0
Resumen
[EN]During literature reviews, and specially when conducting systematic
literature reviews (SLRs), nding and screening relevant papers during scienti c
document search may involve managing and processing large amounts of unstructured
text data. In those cases where the search topic is di cult to establish or has
fuzzy limits, researchers require to broaden the scope of the search and, in consequence,
data from retrieved scienti c publications may become huge and uncorrelated.
However, through a convenient analysis of these data the researcher may be
able to discover new knowledge which may be hidden within the search output,
thus exploring the limits of the search and enhancing the review scope. With that
aim, this paper presents an iterative methodology that applies text mining and
machine learning techniques to a downloaded corpus of abstracts from scienti c
databases, combining automatic processing algorithms with tools for supervised
decision making in an iterative process sustained on the researchers' judgement, so
as to adapt, screen and tune the search output. The paper ends showing a working
example that employs a set of developed scripts that implement the di erent
stages of the proposed methodology
URI
DOI
10.1007/s00500-018-3568-0
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- GRIAL. Artículos [441]