Research on political text metaphor translation system based on web data mining technology

2021 ◽  
pp. 1-10
Author(s):  
Wenjing Wang ◽  
Shanti C. Sandaran

In order to improve the translation effect of political text metaphors, based on Web data mining technology, this paper constructs a political text metaphor translation system based on Web data mining technology. Aiming at the two shortcomings of the selection of the initial center point of the K-Means algorithm and the isolated points, this paper gives a solution to the ICKM algorithm that combines the density parameter and the coordinate rotation algorithm. The algorithm uses the object with the largest density parameter as the first center point, and uses the KCR algorithm to find the next center point, which avoids the influence of isolated points on the data sample to a certain extent. The constructed political text metaphor translation system based on Web data mining technology needs to accurately translate political texts and also needs to meet the requirements of metaphor translation. Finally, this paper designs experiments to verify the system performance. The research results show that the system constructed in this paper can meet the needs of political text metaphor translation.

2014 ◽  
Vol 543-547 ◽  
pp. 3490-3493
Author(s):  
Yan Zhang

With the rapid development of cloud computing technology, the traditional centralized data mining technology becomes inappropriate for the growing huge amounts of data. Cloud computings Web data mining technology comes into use because it is a reliable and efficient method. This article introduces the meaning, characteristics, and the present situation of cloud computing, analyzes the advantage of Web data mining technology on the basis of the use of cloud computing technology, makes investigations and summaries of the present situation, challenges and problems of the current cloud computing Web data mining technology research, and puts forward the corresponding methods to solve these problems.


2014 ◽  
Vol 644-650 ◽  
pp. 2124-2127
Author(s):  
Fen Liu

With the rapid development of Internet, the Internet has become the important resources of information transmission and share. The characteristics of Web data are semi-structured, heterogeneous and mass, making traditional data mining technology indirectly applied to Web data sources. Web data mining refers to extracting a potential, useful model from the Web documents or Web activities. Because of the structural and expansibility of XML, research on XML combined with Web data mining has also became popular.


2021 ◽  
Vol 2066 (1) ◽  
pp. 012033
Author(s):  
Guilian Feng

Abstract With the arrival of the era of big data, people have gradually realized the importance of data. Data is not just a resource, it is an asset. This paper mainly studies the realization of Web data mining technology based on Python. This paper analyzes the overall architecture design of distributed web crawler system, and then analyzes in detail the principles of crawler’s URL function module, crawler’s web crawl function module, crawler’s web page parsing function module, crawler’s data storage function module and so on. Each function module of the crawler system was tested on the experimental computer, and the data information was summarized for comparative analysis. The main significance of this paper lies in the design and implementation of a distributed web crawler system, which, to a certain extent, solves the problems of slow speed, low efficiency and poor scalability of traditional single computer web crawler, and improves the speed and efficiency of web crawler in grasping information and web page data.


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