Accurate Argumentative Zoning with Maximum Entropy models

We present a maximum entropy classifier that significantly improves the accuracy of Argumentative Zoning in scientific literature. We examine the features used to achieve this result and experiment with Argumentative Zoning as a sequence tagging task, decoded with Viterbi using up to four previous classification decisions. The result is a 23% F-score increase on the Computational Linguistics conference papers marked up by Teufel (1999). Finally, we demonstrate the performance of our system in different scientific domains by applying it to a corpus of Astronomy journal articles annotated using a modified Argumentative Zoning scheme.