Combining Contents and Citations for Scientific Document Classification
Lecture notes in computer sciencePublished 1 January 2005
Minh Duc Cao, Xiaoying Gao
Citations28
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
A classification system that exploits the content information as well as citation structure for scientific paper classification, and shows that, combining contents and citations significantly improves the system performance.
Abstract
This paper introduces a classification system that exploits the content information as well as citation structure for scientific paper classification. The system first applies a content-based statistical classification method which is similar to general text classification. We investigate several classification methods including K-nearest neighbours, nearest centroid, naive Bayes and decision trees. Among those methods, the K-nearest neighbours is found to outperform others while the rest perform comparably. Using phrases in addition to words and a good feature selection strategy such as information gain can improve system accuracy and reduce training time in comparison with using words only. To combine citation links for classification, the system proposes an iterative method to update the labellings of classified instances using citation links. Our results show that, combining contents and citations significantly improves the system performance.
Keywords
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