Abstract
The present paper considers the problem of annotating bibliographical references with labels/classes, given training data of references already annotated with labels. The problem is an instance of document categorization where the documents are short and written in a wide variety of languages. The skewed distributions of title words and labels calls for special carefulness when choosing a Machine Learning approach. The present paper describes how to induce Disjunctive Normal Form formulae (DNFs), which have several advantages over Decision Trees. The approach is evaluated on a large real-world collection of bibliographical references.
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Hammarström, H. (2011). Automatic Annotation of Bibliographical References for Descriptive Language Materials. In: Forner, P., Gonzalo, J., Kekäläinen, J., Lalmas, M., de Rijke, M. (eds) Multilingual and Multimodal Information Access Evaluation. CLEF 2011. Lecture Notes in Computer Science, vol 6941. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23708-9_8
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DOI: https://doi.org/10.1007/978-3-642-23708-9_8
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