Abstract
Aiming at the limitation of the existing search engine search algorithm and the low reliability of the derived vocabulary relevancy algorithm, a vocabulary relevancy algorithm based on search engine and vocabulary relevancy algorithm is proposed under the background of making full use of the knowledge base of Hownet. Firstly, the main defects of search engine algorithm are considered in two aspects, meaningless text and redundancy. Then, the two problems are solved by using “Hownet” and DSC algorithm. Finally, the search engine-based vocabulary relevancy algorithm is improved by integrating multiple factors such as weight reduction and noise reduction. Experimental results show that compared with the pure improvement method based on the search engine, the method of spearman coefficient and Pearson coefficient were improved, as well as reduce meaningless language segment on the calculation of correlation, verified the “Hownet”, and DSC algorithm into the search engine algorithm can effectively improve the computing performance of relevant vocabulary.
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