Argumentative Zoning for Improved Citation Indexing

  • Simone Teufel
Part of the The Information Retrieval Series book series (INRE, volume 20)


We address the problem of automatically classifying academic citations in scientific articles according to author affect. There are many ways how a citation might fit into the overall argumentation of the article: as part of the solution, as rival approach or as flawed approach that justifies the current research. Our motivation for this work is to improve citation indexing. The method we use for this task is machine learning from indicators of affect (such as “we follow X in assuming that…”, or “in contrast to Y, our system solves this problem”) and of presentation of ownership of ideas (such as “We present a new method for…”, or “They claim that…”). Some of these features are borrowed from Argumentative Zoning (Teufel and Moens, 2002), a technique for determining the rhetorical status of each sentence in a scientific article. These features include the type of subject of the sentence, the citation type, the semantic class of main verb, and a list of indicator phrases. Evaluation will be both intrinsic and extrinsic, involving the measurement of human agreement on the task and a comparison of human and automatic evaluation, as well as a comparison of task-performance with our system versus task performance with a standard citation indexer (CiteSeer, Lawrence et al., 1999).


citation analysis sentiment machine learning automatic summarisation 


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Copyright information

© Springer 2006

Authors and Affiliations

  • Simone Teufel
    • 1
  1. 1.Computer LaboratoryUniversity of CambridgeCambridgeUK

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