@phdthesis{radoulov2008exploring,
  abstract = { Currently, citation indexes used by digital libraries are very limited. They only provide raw citation counts and link scientific articles through their citations. There are more than one type of citations, but citation indexes treat all citations equally. One way to improve citation indexes is to determine the types of citations in scientific articles (background, support, perfunctory reference, etc.) This will enable researchers to query citation indexes more efficiently by locating articles grouped by citation types. For example, it can enable a researcher to locate all background material needed to understand a specific article by locating all "background" citations. Many classification schemes currently exist. However, manual annotation of all existing digital documents is infeasible because of the sheer magnitude of the digital content, which brings about the need for automating the annotating process, but not much research has been done in the area. One of the reasons preventing researchers from researching automated citation classification is the lack on annotated corpora that they can use. This thesis explores automated citation classification. We make several contributions to the field of citation classification. We present a new citation scheme that is easier to work with than most. Also, we present a document acquisition and citation annotation tool that helps with the development of annotated citation corpora. And finally, we present some experiments with automating citation classification.},
  author = {Radoulov, Radoslav},
  doi = {10012/3712},
  interhash = {5ced35acf5fa742be8d512d93cd2e788},
  intrahash = {91d354c2837da41669de4e85de44cd81},
  month = may,
  school = {School of Computer Science, University of Waterloo},
  title = {Exploring Automatic Citation Classification},
  url = {http://hdl.handle.net/10012/3712},
  year = 2008
}

@inproceedings{marco2004hedging,
  abstract = {Citations in scientific writing fulfil an important role in creating relationships among mutually relevant articles within a research field. These inter-article relationships reinforce the argumentation structure intrinsic to all scientific writing. Therefore, determining the nature of the exact relationship between a citing and cited paper requires an understanding of the rhetorical relations within the argumentative context in which a citation is placed. To automatically determine these relations, we have suggested that various stylistic and rhetorical cues will be significant. One such cue that we are studying is the use of hedging to modify the affect of a scientific claim. We have previously shown that hedging occurs more frequently in citation contexts than in the text as a whole. With this information we conjecture that hedging is a significant aspect of the rhetorical structure of citation contexts and that the pragmatics of hedges may help in determining the rhetorical purpose of citations.},
  author = {Marco, Chrysanne Di and Mercer, Robert E.},
  booktitle = {Proc. AAAI Spring Symposium},
  interhash = {7c4532214d65b2ef202fd2a7d7311901},
  intrahash = {90a6c053558d98c9bfdbb6b4d6d8d725},
  organization = {AAAI},
  title = {Hedging in Scientific Articles as a Means of Classifying Citations},
  url = {http://www.aaai.org/Library/Symposia/Spring/2004/ss04-07-009.php},
  year = 2004
}

@article{nanba2000classification,
  abstract = {We are investigating automatic generation of a review (or survey) article in a specific subject domain. In a research paper, there are passages where the author describes the essence of a cited paper and the differences between the current paper and the cited paper (we call them citing areas). These passages can be considered as a kind of summary of the cited paper from the current author's viewpoint. We can know the state of the art in a specific subject domain from the collection of citing areas. FUrther, if these citing areas are properly classified and organized, they can act 8.', a kind of a review article. In our previous research, we proposed the automatic extraction of citing areas. Then, with the information in the citing areas, we automatically identified the types of citation relationships that indicate the reasons for citation (we call them citation types). Citation types offer a useful clue for organizing citing areas. In addition, to support writing a review article, it is necessary to take account of the contents of the papers together with the citation links and citation types. In this paper, we propose several methods for classifying papers automatically. We found that our proposed methods BCCT-C, the bibliographic coupling considering only type C citations, which pointed out the problems or gaps in related works, are more effective than others. We also implemented a prototype system to support writing a review article, which is based on our proposed method.},
  author = {Nanba, H. and Kando, N. and Okumura, M.},
  interhash = {a8fbc36d3ee8de28f65ef2486bb18cd2},
  intrahash = {7a99ee2d1444ae569beb7bee04137e4b},
  journal = {11th ASIS SIG/CR Classification Research Workshop},
  misc = {10.7152/acro.v11i1.12774},
  pages = {117--134},
  title = {Classification of research papers using citation links and citation types: Towards automatic review article generation},
  url = {http://journals.lib.washington.edu/index.php/acro/article/download/12774/11255},
  year = 2000
}

@article{liu2012fulltext,
  author = {Liu, Xiaozhong and Zhang, Jinsong and Guo, Chun},
  interhash = {011df26355ad51a88947017fd2791a98},
  intrahash = {f9c6133bf4503003822f99860f864698},
  journal = {Journal of the American Society for Information Science and Technology},
  title = {Full-Text Citation Analysis: A New Method to Enhance Scholarly Network},
  url = {http://discern.uits.iu.edu:8790/publication/Full%20text%20citation.pdf},
  year = 2012
}

@inproceedings{aya2005citation,
  abstract = {Citation analysis has been used to study various aspects of scholarly communication. In general, these studies have not differentiated among the multiple reasons for citations. However, authors cite other works for a number of reasons including demonstrating knowledge of the field, establishing the placement of the citing work in the field, comparing and criticizing other works, and paying homage to seminal work by pioneers in the field. In this paper, we present a number of applications in which distinguishing among authors' motivations for citations might be useful and present a machine learning approach to automatically classifying citations according to these motivations. Our approach to citation classification makes use of the structure and the argumentative nature of the scientific papers. We present the results of experiments we ran on papers in the computer science field. The results are encouraging and give us hope that we can use our citation classifier in analyzing large corpora of scientific papers.},
  author = {Aya, Selcuk and Lagoze, Carl and Joachims, Thorsten},
  booktitle = {Proceedings of the International Conference on Knowledge Management},
  chapter = 24,
  doi = {10.1142/9789812701527_0024},
  eprint = {http://www.worldscientific.com/doi/pdf/10.1142/9789812701527_0024},
  interhash = {f35b1f099571f3f134186ff407ee5fee},
  intrahash = {d30bac9f744e0473499f1d15d55258b8},
  month = oct,
  pages = {287--298},
  publisher = {World Scientific Publishing},
  title = {Citation Classification and its Applications},
  url = {http://www.worldscientific.com/doi/abs/10.1142/9789812701527_0024},
  year = 2005
}