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).
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Merity et al. (2009) studied this question.