An Approach to Text Corpus Construction which Cuts Annotation Costs and Maintains Reusability of Annotated Data
Katrin Tomanek, Joachim Wermter, Udo Hahn · 2007
We consider the impact Active Learning (AL) has on effective and efficient text corpus annotation, and report on reduction rates for annotation efforts ranging up until 72%. We also address the issue whether a corpus annotated by means of AL – using a particular classifier and a particular feature set – can be re-used to train classifiers different from the ones employed by AL, supplying alternative feature sets as well. We, finally, report on our experience with the AL paradigm under real-world conditions, i.e., the annotation of large-scale document corpora for the life sciences. 1