| Title: | Automatic classification of older electronic texts into the Universal Decimal Classification-UDC |
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| Authors: | ID Kragelj, Matjaž (Author)
ID Kljajić Borštnar, Mirjana (Author) |
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| Files: |  Kragelj-2021-Automatic_classification_of_older.pdf (1,91 MB)
MD5: 208C31917DFFA5DE7BAAB352534378A0
 https://doi.org/10.1108/JD-06-2020-0092
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FOV - Faculty of Organizational Sciences in Kranj
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| 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. |
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| Keywords: | digital library, artificial intelligence, machine learning, text classification, older texts, Universal Decimal Classification |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 09.06.2020 |
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| Article acceptance date: | 30.10.2020 |
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| Publication date: | 08.04.2021 |
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| Publisher: | Aslib |
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| Year of publishing: | 2021 |
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| Number of pages: | Str. 755-776 |
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| Numbering: | Letn. 77, št. 3 |
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| PID: | 20.500.12556/DKUM-91682  |
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| UDC: | 004.89:025.45UDC |
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| ISSN on article: | 0022-0418 |
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| COBISS.SI-ID: | 41547267 |
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| DOI: | 10.1108/JD-06-2020-0092 |
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| Publication date in DKUM: | 28.01.2025 |
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| Views: | 157 |
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| Downloads: | 14 |
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| Metadata: |    |
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| Categories: | Misc.
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