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

The purpose of this paper is to present a topic classification model using the Dewey Decimal Classification (DDC) as the target scheme. This is to be done by exploring metadata as provided by the Open Archives Initiative (OAI) to derive document snippets as minimal document representations. The reason is to reduce the effort of document processing in digital libraries. Further, the paper seeks to perform feature selection and extension by means of social ontologies and related web‐based lexical resources. This is done to provide reliable topic‐related classifications while circumventing the problem of data sparseness. Finally, the paper aims to evaluate the model by means of two language‐specific corpora. 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 in digital libraries based on closed topic models such as the DDC.

The approach takes the form of text classification, text‐technology, computational linguistics, computational semantics, and social semantics.

It is shown that SVM‐based classifiers perform best by exploring certain selections of OAI document metadata.

The findings show that it is necessary to further develop SVM‐based DDC‐classifiers by using larger training sets possibly for more than two languages in order to get better F‐measure values.

Algorithmic and formal‐mathematical information is provided on how to build DDC‐classifiers for digital libraries.