The paper focuses on the process of selecting representative sample documents written in a natural language that can be used as the basis for automatic selection or classification of textual documents. A method of selecting the examples from a larger set of candidate examples, called automatic biased sample selection, is compared to random and manual selection. The methods are evaluated by experiments carried out with real world data consisting of customer reviews, with different document representations and similarity measures. Presented approach, that provided satisfactory results, faces problems related to processing user created content and huge computational complexity and can be used as an alternative to manual selection and evaluation of textual samples.
Publié le : 2013-11-18
Classification:
other areas of Computing and Informatics,
text classification, textual patterns, machine learning, natural language processing, text similarity, information retrieval,
68U15, 68T50
@article{cai902,
author = {Frantisek Darena; Mendel University Brno and Jan Zizka; Mendel University Brno},
title = {Approaches to samples selection for machine learning based classification of textual data},
journal = {Computing and Informatics},
volume = {31},
number = {6},
year = {2013},
language = {en},
url = {http://dml.mathdoc.fr/item/cai902}
}
Frantisek Darena; Mendel University Brno; Jan Zizka; Mendel University Brno. Approaches to samples selection for machine learning based classification of textual data. Computing and Informatics, Tome 31 (2013) no. 6, . http://gdmltest.u-ga.fr/item/cai902/