Abstract
The number of electronic journal articles is growing faster than ever before; information is generated faster than people can deal with it. In order to handle this problem, many electronic periodical databases have proposed keyword search methods to decrease the effort and time spent by users in searching the journal’s archives. However, the users still have to deal with a huge number of search results. How to provide an efficient search, i.e., to present the search results in categories, has become an important current research issue. If search results can be classified and shown by their topics, users can find papers of interest quickly. However, traditional topic detection methods use only word frequencies, ignoring the importance of semantics. In addition, the bibliographic structures (e.g., Title, Keyword, and Abstract) have particular importance. Therefore, this paper describes a topic detection method based on bibliographic structures and semantic properties to extract important words and cluster the scholarly literature. The experimental results show that our method is better than the traditional method.
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Wang, HC., Huang, TH., Guo, JL., Li, SC. (2009). Journal Article Topic Detection Based on Semantic Features. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_65
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DOI: https://doi.org/10.1007/978-3-642-02568-6_65
Publisher Name: Springer, Berlin, Heidelberg
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