A Persian Citation Parsing Method Using Support Vector Machine

Authors

Abstract

Human users can easily divide a bibliographic reference to its constructing fields such as authors, title, journal, year, etc. However, due to the variations in formats and errors made by the authors in citing documents, it is difficult to automate this task. There exist many solutions for this problem, known as citation parsing problem in the literature, however, none of them is compatible with Persian language. This is mainly due to high language-sensitivity of these solutions. Considering the important role of citation parsing in tasks such as autonomous citation indexing and information retrieval, in this paper, we propose an intelligent method for citation parsing in Persian language. The proposed method uses the support vector machine (SVM) classification method as its core. The results of testing the proposed method using a dataset designed for this task show 95% in average for precision, recall and F1 measures for extracting different fields from a bibliographic reference which is quite plausible.

Keywords


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