基于标准时序生成的科研热点预测及加速方法

A Prediction and Acceleration Approach of Scientific Research Hotspot Based on Standard Sequence Generation

目前科研热点预测严重依赖于本领域高级专业人员通过大量文献查阅与市场调研的方法确定。随着科技文章及文献数量的迅速增长,单纯人工跟踪和分析科研热点变得困难。简单的统计方式如关键词和文本中词频较高的词来表示热点话题,耗时且准确性欠佳。基于机器学习算法且考虑数据时序变化的关系,提出一种科研热点预测算法。同时,为了缩短算法运行时间、提高执行效率,一种基于阈值的预测加速方法被加入预测框架。实验表明,提出的科研热点预测算法较基准算法查全率平均提高25.75%,查准率平均提高28.25%。

At present,predicting hotspots depends on document consulting and market survey by advanced professionals.With the rapid growth of scientific documents and literatures,it becomes difficult to artificially trace and analyze scientific research hotspots.Simple statistics on keywords or frequencies is time-consuming and their accuracy is low.In the paper we propose a novel prediction approach of scientific research hotspot based on artificial intelligence algorithm,which considers the data changes over time.Moreover,we add an acceleration step into prediction framework using threshold method in order to improve execution efficiency.Our extensive experiments demonstrate that our proposed algorithm has 25.75%better recall ratio and 28.25%higher precision ratio than benchmark algorithm.