Enhancing document modeling by means of open topic models Crossing the frontier of classification schemesin digital libraries by example of the DDC

摘要

Purpose – The purpose of this paper is to present a topic classification model using the Dewey DecimalClassification (DDC) as the target scheme. This is to be done by exploring metadata as provided by theOpen Archives Initiative (OAI) to derive document snippets as minimal document representations. Thereason is to reduce the effort of document processing in digital libraries. Further, the paper seeks toperform feature selection and extension by means of social ontologies and related web-based lexicalresources. This is done to provide reliable topic-related classifications while circumventing the problemof data sparseness. Finally, the paper aims to evaluate the model by means of two language-specificcorpora. The paper bridges digital libraries, on the one hand, and computational linguistics, on the other.The aim is to make accessible computational linguistic methods to provide thematic classifications indigital libraries based on closed topic models such as the DDC. Design/methodology/approach – The approach takes the form of text classification,text-technology, computational linguistics, computational semantics, and social semantics. Findings .-is shown that SVM-based classifiers perform best by exploring certain selections ofOAI document metadata. Research limitations/implications – The findings show that it is necessary to further developSVM-based DDC-classifiers by using larger training sets possibly for more than two languages inorder to get better F-measure values. Originality/value – Algorithmic and formal-mathematical information is provided on how to buildDDC-classifiers for digital libraries.