Desarrollo y evaluación de herramientas basadas en Procesamiento de Lenguaje Natural e Inteligencia Artificial para el tratamiento de especificaciones técnicas y no técnicas con el fin de mejorar la calidad, extraer información y consolidar el conocimiento
Browsing Desarrollo y evaluación de herramientas basadas en Procesamiento de Lenguaje Natural e Inteligencia Artificial para el tratamiento de especificaciones técnicas y no técnicas con el fin de mejorar la calidad, extraer información y consolidar el conocimiento by Subject "document clustering"
(Springer, Cham, 2025-6-19)
Nusch, Carlos Javier; Del Río Riande, Gimena; Cagnina, Leticia Cecilia; Errecalde, Marcelo Luis; Antonelli, Ruben Leandro
This article describes various Automatic Text Analysis tasks
applying Natural Language Processing techniques on a corpus of Latin texts
from the 1st century BC and 1st century AD. The motivation behind this work
is to delve into and understand a historical literary trend revolving around the
themes of love, spanning from antiquity through to the medieval period. The
analyzed authors include Gaius Valerius Catullus, Albius Tibullus, and Sextus
Propertius, who represent the literary movement of the neoterics, as a group of
poets to be identified, and Publius Vergilius Maro and Marcus Annaeus
Lucanus, epic poets with remarkably distinct styles, as control samples. The
purpose of this preliminary and exploratory study is to investigate the potential
and best features for document clustering. The clustering tasks were carried out
using fixed ranges of character n-grams and word n-grams. For the clustering
tasks, the K-Means method and the Silhouette Index were used for determining
the optimal cluster sizes. Using optimal clusters as labels, decision trees were
trained for each range of n-grams, aiming to identify features with the highest
Information Gain and Information Gain Ratio. The trees were trained based on
the criterion of Entropy, and calculations of Feature Importance were
performed. Results show variations based on text preprocessing techniques:
simple filtering of stopwords in the corpus yields better Silhouette scores, with
one or two features showing potential classification value for the decision trees.
The application of TF-IDF weighting results in Silhouette indices closer to
zero, albeit with a more balanced distribution of Importance among different
features.