Abstract
Constructing theme-related word set is a basic work for establishing theme-oriented information retrieval systems. Nowadays, most of previous studies focus on identifying representative words of a specific document, and few studies pay attention to constructing a word set related to a theme. By analyzing existing keywords extraction methods, this paper proposes a method to automatically construct theme-related word set based on the primary theme-related word set given by domain experts and the well-known websites related to the theme. As the first step, the method uses existing information extraction techniques to obtain the documents from the websites and every document’s keyword set. Then it calculates the correlation degree between the known theme-related word set and the document keyword set, further gets a word set of the document related to the theme based on the document-theme relevance, and merges the word set to the theme-related word set. By using the method, the theme-related word set is supplemented by iteration based on the documents gotten from the theme-related websites. Because there is little research work focusing on this problem and no relevant experimental data set, this paper uses the proposed method to construct theme-related word sets towards two themes “electricity” and “college entrance examination”, and we invite domain experts to evaluate the word sets. The results show that a relatively complete theme-related word set can be obtained based on this method, which shows the feasibility of our methods.
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Acknowledgement
This research was supported by the Training plan of Tianjin University Innovation Team (No.TD13-5025), the Natural Science Foundation of Tianjin (No.15JCYBJC46500) and the Major Project of Tianjin Smart Manufacturing (No.15ZXZNCX00050).
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Wu, Y., Li, Y., Hao, G. (2018). A Web-Based Theme-Related Word Set Construction Algorithm. In: U, L., Xie, H. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 11268. Springer, Cham. https://doi.org/10.1007/978-3-030-01298-4_17
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DOI: https://doi.org/10.1007/978-3-030-01298-4_17
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