Abstract
Citation function is dened 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.
| Original language | English |
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| Title of host publication | Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP'06) |
| Subtitle of host publication | Discourse |
| Place of Publication | Morristown, NJ, USA |
| Publisher | ACL |
| Pages | 103-110 |
| ISBN (Print) | 1932432736 |
| Publication status | Published - 2006 |
| Event | 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006) - Sydney, Australia Duration: 22 Jul 2006 → 23 Jul 2006 |
Conference
| Conference | 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006) |
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| Country/Territory | Australia |
| City | Sydney |
| Period | 22/07/06 → 23/07/06 |