Automatic classification of older electronic texts into the Universal Decimal Classification-UDC

Matjaž Kragelj (Author), Mirjana Kljajić Borštnar (Author)

Abstract

Purpose:The purpose of this study is to develop a model for automated classification of old digitised texts to the Universal Decimal Classification (UDC), using machine-learning methods. Design/methodology/approach: The general research approach is inherent to design science research, in which the problem of UDC assignment of the old, digitised texts is addressed by developing a machine-learning classification model. A corpus of 70,000 scholarly texts, fully bibliographically processed by librarians, was used to train and test the model, which was used for classification of old texts on a corpus of 200,000 items. Human experts evaluated the performance of the model. Findings: Results suggest that machine-learning models can correctly assign the UDC at some level for almost any scholarly text. Furthermore, the model can be recommended for the UDC assignment of older texts. Ten librarians corroborated this on 150 randomly selected texts. Research limitations/implications: The main limitations of this study were unavailability of labelled older texts and the limited availability of librarians. Practical implications: The classification model can provide a recommendation to the librarians during their classification work; furthermore, it can be implemented as an add-on to full-text search in the library databases. Social implications: The proposed methodology supports librarians by recommending UDC classifiers, thus saving time in their daily work. By automatically classifying older texts, digital libraries can provide a better user experience by enabling structured searches. These contribute to making knowledge more widely available and useable. Originality/value: These findings contribute to the field of automated classification of bibliographical information with the usage of full texts, especially in cases in which the texts are old, unstructured and in which archaic language and vocabulary are used.

Keywords

digitalna knjižnica;umetna inteligenca;stojno učenje;klasifikacija besedil;starejše besedilo;Univerzalna decimalna klasifikacija;

Data

Language:	English
Year of publishing:	2021
Typology:	1.01 - Original Scientific Article
Organization:	UM FOV - Faculty of Organizational Sciences
Publisher:	Aslib
UDC:	004.89:025.45UDC
COBISS:	41547267
ISSN:	0022-0418
Views:	0
Downloads:	0
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Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	digital library;artificial intelligence;machine learning;text classification;older texts;Universal decimal classification;
Type (COBISS):	Scientific work
Pages:	str. 755-776
Volume:	ǂVol. ǂ77
Issue:	ǂno. ǂ3
Chronology:	2021
DOI:	10.1108/JD-06-2020-0092
ID:	25795144