Text Representation for Efficient Document Annotation

Christin Seifert (Passau University, Germany)

Eva Ulbrich (Know-Center Graz, Austria)

Roman Kern (Know-Center Graz, Austria)

Michael Granitzer (Passau University, Germany)

Abstract: In text classification the amount and quality of training data is crucial for the performance of the classifier. The generation of training data is done by human labellers - a tedious and time-consuming work. To reduce the labelling time for single documents we propose to use condensed representations of text documents instead of the full-text document. These condensed representations are key sentences and key phrases and can be generated in a fully unsupervised way. We extended and evaluated the TextRank algorithm to automatically extract key sentences and key phrases. For representing key phrases we propose a layout similar to a tag cloud. In a user study with 37 participants we evaluated whether document labelling with these condensed representations can be done faster and equally accurate by the human labellers. Our evaluation shows that the users labelled tag clouds twice as fast and as accurately as full-text documents. While further investigations for different classification tasks are necessary, this insight could potentially reduce costs for the labelling process of text documents.

Keywords: TextRank, data mining, document labelling, supervised learning, tag clouds, text summarisation, word clouds

Categories: H.1.2, H.1.7