Abstract
PurposeThe 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/approachThe 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.FindingsResults 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/implicationsThe main limitations of this study were unavailability of labelled older texts and the limited availability of librarians.Practical implicationsThe 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 implicationsThe 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/valueThese 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.
Highlights
Written sources are a cornerstone of cultural heritage and provide evidence of human creativity, development and culture in specific times and spaces
In the following, we report the results of the clustering analysis, classification model building and testing on newer scholarly data and use on the older texts
4.1 Clustering of the scholarly articles data set In the bibliographic catalogue, all the articles are classified with the usage of Universal Decimal Classification (UDC); it was straightforward to check whether the naturally occurring clusters in the scholarly corpus are aligned with the assigned UDC class
Summary
Written sources are a cornerstone of cultural heritage and provide evidence of human creativity, development and culture in specific times and spaces They are kept and cared for at libraries. It is estimated that several hundred thousand texts, published in the 19th and 20th centuries, will not be manually processed, nor will librarians produce bibliographic records in the library catalogue for those. Because these types of sources will probably not be catalogued (in contrast to scholarly articles, for which a large set of metadata is available), it will be difficult or even impossible to offer filters and faceted navigation (because the lack of available metadata, including classification, such as UDC)
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