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Argumentative Zoning for Improved Citation Indexing

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

Abstract

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).

Keywords

citation analysis sentiment machine learning automatic summarisation 

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8. Bibliography

  1. Computation and Language Archive (1994). http://xxx.lanl.gov/cmp-lgGoogle Scholar
  2. Ge, N. Hale, J., and Charniak, E. (1998). A statistical approach to anaphora resolution. In Proceedings of the Sixth Workshop on Very Large Corpora.Google Scholar
  3. Giles, C, Bollacker, K., and Lawrence, S. (1998). Citeseer: An automatic citation indexing system. In Proceedings of the Third ACM Conference on Digital Libraries.Google Scholar
  4. Kupiec, J., Pedersen, J., and Chen, F. (1995). A trainable document summarizer. In Proceedings of the 18thAnnual International Conference on Research and Development in Information Retrieval (SIGIR-95).Google Scholar
  5. Lawrence, S., Giles, C., and Bollacker, K. (1999). Digital libraries and autonomous citation indexing. IEEE Computer 32(6).Google Scholar
  6. Lewis, D. (1991). Evaluating text categorisation. In Speech and Natural Language: Proceedings of the ARPA Workshop of Human Language Technology.Google Scholar
  7. Myers, G. (1992). In this paper we report…-speech acts and scientific facts. Journal of Pragmatics 17(4).Google Scholar
  8. Nanba and Okumura (1999). Towards multi-paper summarization using reference information. In Proceedings of IJCAI-99.Google Scholar
  9. Pang, B., Lee, L., and Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP).Google Scholar
  10. Shum, S. (1998). Evolving the web for scientific knowledge: First steps towards an “HCI knowledge web”. Interfaces, British HCI Group Magazine 39.Google Scholar
  11. Siegel, S. and Castellan, N. (1988). Nonparametric Statistics for the Behavioral Sciences. Berkeley, CA: McGraw-Hill, 2nd edition.Google Scholar
  12. Swales, J. (1990). Genre Analysis: English in Academic and Research Settings. Chapter 7: Research articles in English. Cambridge, UK: Cambridge University Press.Google Scholar
  13. Teufel, S. and Moens, M. (2002). Summarising scientific articles — experiments with relevance and rhetorical status. Computational Linguistics 28(4).Google Scholar
  14. Teufel, S., Carletta, J., and Moens, M. (1999). An annotation scheme for discourse-level argumentation in research articles. In Proceedings of the Ninth Meeting of the European Chapter of the Association for Computational Linguistics (EACL-99).Google Scholar
  15. Teufel, S. (2001). Task-based evaluation of summary quality: Describing relationships between scientific papers. In Proceedings of NAACL-01 Workshop “Automatic Text Summarization”.Google Scholar
  16. Weinstock, M. (1971). Citation Indexes. In Encyclopaedia of Library and Information Science, volume 5, New York, NY: Dekker.Google Scholar
  17. Wiebe, J. (1994). Tracking point of view in narrative. Computational Linguistics 20(2).Google Scholar

Copyright information

© Springer 2006

Authors and Affiliations

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

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