Scientific papers revolve around citations , and for many discourse level tasks one needs to know whose work is being talked about at any point in the discourse . In this paper , we introduce the scientific attribution task , which links different linguistic expressions to citations . We discuss the suitability of different evaluation metrics and evaluate our classification approach to deciding attribution both intrinsically and in an extrinsic evaluation where information about scientific attribution is shown to improve performance on Argumentative Zoning , a rhetorical classification task . In the recent past , there has been a focus on information management from scientific literature . In the genetics domain , for instance , information extraction of genes and gene-protein interactions helps geneticists scan large amounts of information ( e.g. , as explored in the TREC Genomics track ( Hersh et al . , 2004 ) ) . Elsewhere , citation indexes ( Garfield , 1979 ) provide bibliometric data about the frequency with which particular papers are cited . The success of citation indexers such as CiteSeer ( Giles et al . , 1998 ) and Google Scholar relies on the robust detection of formal citations in arbitrary text . In bibliographic information retrieval , anchor text , i.e. , the context of a citation can be used to characterise ( index ) the cited paper using terms outside of that paper ( Bradshaw , 2003 ) ; O'Connor ( 1982 ) presents an approach for identifying the area around citations where the text focuses on that citation . And automatic citation classification ( Nanba and Okumura , 1999 ; Teufel et al . , 2006 ) determines the function that a citation plays in the discourse . For such information access and retrieval purposes , the relevance of a citation within a paper is often crucial . One can estimate how important a citation is by simply counting how often it occurs in the paper . But as Kim and Webber ( 2006 ) argue , this ignores many expressions in text which refer to the cited author 's work but which are not as easy to recognise as citations . They address the resolution of instances of the third person personal pronoun " they " in astronomy papers : it can either refer to a citation or to some entities that are part of research within the paper ( e.g. , planets or galaxies ) . Several applications should profit in principle from detecting connections between referring expressions and citations . For instance , in citation function classification , the task is to find out if a citation is described as flawed or as useful . Consider : Most computational models of discourse are based primarily on an analysis of the intentions of the speakers [ Cohen and Perrault , 1979 ] [ Allen and Perrault , 1980 ] [ Grosz and Sidner , 1986 ] WEAK . The speaker will form intentions based on his goals and then act on these intentions , producing utterances . The hearer will then reconstruct a model of the speaker 's intentions upon hearing the utterance . This approach has many strong points , but does not provide a very satisfactory account of the adherence to discourse conventions in dialogue . The three citations above are described as flawed ( detectable by " does not provide a very satisfactory account " ) , and thus receive the label Weak . However , in order to detect this , one must first realise that " this approach " refers to the three cited papers . A contrasting hypothesis could be that the citations are used ( thus deserving the label Use ; the cue phrase " based on " might make us think so ( as in the context " our work is based on " ) . This , however , can be ruled out if we know that " the speaker " is not referring to some aspect of the current paper . We have described a new reference task -deciding scientific attribution , and demonstrated high human agreement ( α > 0.8 ) on this task . Our machine learning solution using shallow features achieves an agreement of α M = 0.68 with the human gold standard , increasing to α M = 0.71 if only pronouns need to be resolved . We have also demonstrated that information about scientific attribution improves results for a discourse classification task ( Argumentative Zoning ) . We believe that similar improvements can be achieved on other discourse annotation tasks in the scientific literature domain . In particular , we plan to investigate the use of scientific attribution information for the citation function classification task .

Challenge: Relevance of a citation in a paper to the cited paper is crucial information for retrieval purposes but computing occurrences would result in low recalls.
Approach: They propose the scientific attribution task which links different linguistic expressions to citations and explore both intrinsic and extrinsic evaluation setups.
Outcome: They show that high human agreement on the proposed task and information about scientific attributions can improve the performance of discourse classification.