Accurate argumentative zoning with maximum entropy models

Authors:
Stephen Merity;Tara Murphy;James R. Curran
Affiliations:
University of Sydney, NSW, Australia;University of Sydney, NSW, Australia;University of Sydney, NSW, Australia
Venue:
NLPIR4DL '09 Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries
Year:
2009

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Abstract

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.