基于关键词抽取算法的隐喻研究趋势分析  

Reviewing Metaphor Research Based on Keyword Extraction Algorithm

作　　者：张冬瑜[1] 顾丰 崔紫娟 胡绍翔 张伟 林鸿飞[3] Zhang Dongyu;Gu Feng;Cui Zijuan;Hu Shaoxiang;Zhang Wei;Lin Hongfei(School of Software,Dalian University of Technology,Dalian 116620,China;International Office,Dalian University of Technology,Dalian 116024,China;School of Computer Science and Technology,Dalian University of Technology,Dalian 116023,China)

机构地区：[1]大连理工大学软件学院,大连116620 [2]大连理工大学国际合作与交流处,大连116024 [3]大连理工大学计算机科学与技术学院,大连116023

出　　处：《数据分析与知识发现》2022年第4期130-138,共9页Data Analysis and Knowledge Discovery

基　　金：辽宁省社会科学规划基金项目(项目编号:L20BYY023)的研究成果之一。

摘　　要：【目的】针对中国近40年隐喻研究的发展和演变规律进行梳理和定量分析,旨在为相关语言学家和计算语言学家提供参考,缩小中国隐喻研究与国外先进水平的差距。【方法】采用关键词抽取算法将隐喻文献映射为关键词集合,结合科学计量学原理筛选出6个有效特征作为回归模型参数预测下一年的热点词频度,对隐喻发展进行历时和共时分析。【结果】对比5种回归模型预测结果,发现拟合程度最好的梯度上升回归树模型对下一年度的关键词预测精度最高,特征消融实验的结果也证实所选的每一个特征均有效。【局限】关键词抽取算法的准确性有待进一步提高。【结论】隐喻研究正在向多领域、多学科交叉的方向发展。本文特征选择的方法可以为隐喻自动识别研究提供参考。[Objective]This paper reviews the developments of metaphor research in China in the past 40 years,aiming to provide references for linguists and then narrow the gaps between Chinese and foreign researchers.[Methods]First,we used keywords extraction algorithms to map metaphor documents into keyword sets.Then,we chose effective features as parameters for the regression models,which helped us predict the frequency of trending words in the next year.Finally,we analyzed the developments of metaphor research diachronically and synchronously.[Results]Our study compared the results of five regression models.Among them,the GBR model with the best fitting degree had the highest prediction accuracy for next year’s trending words.The feature ablation experiment also confirmed that our selected features were effective.[Limitations]The accuracy of keyword extraction algorithm could be optimized.[Conclusions]Metaphor research is developing towards the direction of cross-domains and inter-disciplines.The method of feature selection provides more references for research in prediction models.

关 键 词：隐喻 热点预测 回归模型 历时分析 共时分析 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]