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ASME Press Select Proceedings

International Conference on Computer Engineering and Technology, 3rd (ICCET 2011)

By
Jianhong Zhou
Jianhong Zhou
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ISBN:
9780791859735
No. of Pages:
970
Publisher:
ASME Press
Publication date:
2011

Finding context specific, task oriented research papers is crucial for researchers. To find the most relevant papers supports a number of tasks such as: one is starting research in an area, one need to find seminal paper in an area, one looking for collaborations, and one who wants to explore the most relevant papers for one's research area. There are number of techniques available to find relevant papers such as search engines, citation indexes, social websites etc. However, the contemporary systems display too many options for a researcher. For example, if one uses citation index such as Google Scholar, then one comes to know that these are 1000 papers that have cited the focused paper. In that case, researchers need to explore all of the citations and after reading all of the 1000 citations, researchers may conclude that out of these 1000 papers, only 5 papers are actual extending the work mentioned in the cited paper and are closely related. While other citations are not actually relevant with the cited paper — just giving a background study of the area etc. In order to find out how papers are related with one another, the very first step is to know the motivation of the author to cite a particular paper. There are a number of reasons for citing the documents present in the literature. We refer to these reasons as citation functions. Therefore, an automatic system can be developed that can analyze, structure, and present the most relevant papers to researchers if we could make a classification of citations functions. In this paper, we have made a classification of different reasons for citations. This paper contributes in making such a classification. We find out reasons by manually exploring the citing sentences of about 60 research papers. Some existing reasons in the literature enhanced our classification. We group these possible reasons into different classes and give them a structure. This structure will help out in building the rules that will help in automatic semantic analysis of the papers.

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