The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
Proceedings of the 10th annual joint conference on Digital libraries
AWC '13 Proceedings of the First Australasian Web Conference - Volume 144
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Scientific citations play a crucial role in maintaining the network of relationships among mutually relevant articles within a research field. Customarily, authors include citations in their papers to indicate works that are foundational in their field, background for their own work, or representative of complementary or contradictory research. But, determining the nature of the exact relationshipb etween a citing and cited paper is often difficult to ascertain. To address this problem, the aim of formal citation analysis has been to categorize and, ultimately, automatically classify scientific citations. In previous work, Garzone and Mercer (2000) presented a system for citation classification that relied on characteristic syntactic structure to determine citation category. In this present work, we extend this idea to propose that fine-grained cue phrases within citation sentences may provide a stylistic basis for just such a categorization.