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We study the interplay of the discourse struc- ture of a scientic argument with formal ci- tations. One subproblem of this is to clas- sify academic citations in scientic articles ac- cording to their rhetorical function, e.g., as a rival approach, as a part of the solution, or as a awed approach that justies the cur- rent research. Here, we introduce our anno- tation scheme with 12 categories, and present an agreement study

The automatic recognition of the rhetori- cal function of citations in scientic text has many applications, from improvement of impact factor calculations to text sum- marisation and more informative citation indexers. Citation function is dened as the author's reason for citing a given pa- per (e.g. acknowledgement of the use of the cited method). We show that our annotation scheme for citation func- tion is reliable, and present a supervised machine learning framework to automati- cally classify citation function, which uses several shallow and linguistically-inspired features. We nd, amongst other things, a strong relationship between citation func- tion and sentiment classication.