专利大数据环境下基于改进WRD的精准匹配方法

The existing patent inventory capacity in China is large, but the lack of efficient and accurate matching processing technology has hindered the further improvement of the patent conversion rate. To solve this problem, the natural language processing was introduced to propose an accurate matching technology in the patented big data environment. Each provincial patents data was distributed storage in the Hadoop File Systems (HDFS), and the distributed parallel processing architecture was used to improve the processing performance. In addition, the improved Word Rotator's Distance(WRD)algorithm was used, and the traditional bidirectional movement was re-defined as the movement from the smaller side to the larger total weight by restricting the direction of word shift process. The objective function was modified by considering a penalty term, which was the cosine similarity of the two total weight. By dropping the improved WRD, the computational complexity of total weight was reduced and the accuracy of the natural language matching was improved, which provided an effective method on accurate matching under the patent big data.