Abstract
Citation function is defined as the author's reason for citing a given paper (e.g. acknowledgement of the use of the cited method). The automatic recognition of the rhetorical function of citations in scientific text has many applications, from improvement of impact factor calculations to text summarisation and more informative citation indexers. We show that our annotation scheme for citation function is reliable, and present a supervised machine learning framework to automatically classify citation function, using both shallow and linguistically-inspired features. We find, amongst other things, a strong relationship between citation function and sentiment classification. © 2006 Association for Computational Linguistics.
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CITATION STYLE
Teufel, S., Siddharthan, A., & Tidhar, D. (2006). Automatic classification of citation function. In COLING/ACL 2006 - EMNLP 2006: 2006 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (pp. 103–110). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1610075.1610091
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