The impact of corpus quality and type on topic based text segmentation evaluation

Abstract

In this paper, we try to fathom the real impact of corpus quality on methods performances and their evaluations. The considered task is topic-based text segmentation, and two highly different unsupervised algorithms are compared: C 99, a word-based system, augmented with LSA, and Transeg, a sentence-based system. Two main characteristics of corpora have been investigated: Data quality (clean vs raw corpora), corpora manipulation (natural vs artificial data sets). The corpus size has also been subject to variation, and experiments related in this paper have shown that corpora characteristics highly impact recall and precision values for both algorithms.