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Search Terms Construction for Financial Advertisements Acquisition in Search Engines

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DOI: 10.23977/AICT2020022


Qun Dong, Changyong Guo, Zhaoxin Zhang and Ning Li

Corresponding Author


Internet financial advertisements have become the top priority in the digital economy and financial advertisements acquisition is the data basis for all relevant researches. We will study on the construction of search terms, a primary task of obtaining financial advertisements in search engines, which is a problem related to semantic extension. In this paper, an unsupervised, graph-based algorithm that simultaneously incorporates the position of words and their frequency in documents to choose search terms for financial advertisements acquisition is proposed. Experiments are carried out to prove that the algorithm compared with other algorithms is more efficient in terms of the time performance and can find more financial advertisements in search engines.


Nature language processing; graph-based algorithm; search terms construction

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