Similarity Computation of Patent Phrases Based on Knowledge Injection Prompt Learning
A patent is a legal right conferred to inventors to protect their inventions for a limited time,and it plays a crucial role in present-day social activities.Existing research has not optimized the adaptation of patent similarity data,which has negatively affected matching patent phrase similarity.Previous research has shown that in low-resource scenarios,prompt learning uses text fragments(i.e.,templates)as input,transforming the classification problem into a mask language modeling problem;here,a key step is to construct a projection between the label space and label word space.This study presents a knowledge-based prompt learning method and applies it to the similarity matching of patent phrases.To solve the problem of insufficient information related to patent phrases,this study uses similarity label information in patent phrases and knowledge to enhance the patent phrases and label information.This study first establishes the relationship between patent phrases and external knowledge using entity-linking technology.The study then designs a neighborhood information filtering mechanism based on the degree of entity influence to expand the problem of insufficient patent phrase information.Finally,based on the effects of different types of external knowledge on the similarity calculation of patent phrases,the study generates a variety of enhanced prompt text applied to patent phrases.Experimental results show that the Pearson Correlation Coefficient(PCC)and Spearman Rank Correlation(SRC)of the proposed method are increased by 6.8％and 5.7％,respectively,as compared with the suboptimal method.
patent phrasesimilarity computationknowledge injectionprompt learningprompt text
万方数据
维普
