基于Transformer和BERT的名词隐喻识别  

Identifying Noun Metaphors with Transformer and BERT

作　　者：张冬瑜[1] 崔紫娟 李映夏[1] 张伟 林鸿飞[3] Zhang Dongyu;Cui Zijuan;Li Yingxia;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》2020年第4期100-108,共9页Data Analysis and Knowledge Discovery

基　　金：教育部人文社会科学基金项目“基于机器学习的情感隐喻识别研究”(项目编号:16YJCZH141);国家自然科学基金项目“基于语义资源和深度学习的情感隐喻识别方法研究”(项目编号:61602079);国家自然科学基金重点项目“社交媒体中文本情感语义计算理论和方法”(项目编号:61632011)的研究成果之一。

摘　　要：【目的】解决名词隐喻识别研究中语义信息利用不足和关系表征的问题,提高识别效果。【方法】使用BERT模型替代词向量,在语义表示中同时包含词与词之间的位置关系等信息,利用Transformer模型进行特征提取并通过神经网络分类器进行识别。【结果】本文模型在准确率(0.900 0)、精确率(0.896 4)、召回率(0.885 8)和F1值(0.891 0)4个指标上均表现最好,可以注意到多个关键点信息,提高名词隐喻的分类效果。【局限】对于中文文本中的冷僻词汇、成语古语以及干扰词汇等的判断比较困难。【结论】本文所提隐喻识别方法优于现有基于人工特征的分类模型及主流深度学习模型。[Objective] This paper proposes a new method to address the issues facing semantic information and relationship representation, aiming to improve the recognition of noun metaphors. [Methods] First, we used the BERT model to replace the word vector, and added position relationship among words for the semantic representation. Then, we utilized the Transformer model to extract features. Finally, we identified the noun metaphors with the help of used neural network classifier. [Results] The proposed model got the highest scores in accuracy(0. 900 0), precision(0. 896 4), recall(0. 885 8), and F1(0. 891 0). It covered multiple key points to improve the classification results of noun metaphors. [Limitations] The proposed method could not process the Chinese ancient idioms, as well as rare or dummy vocabularies. [Conclusions] The proposed model could more effectively identify Noun Metaphors than the existing models based on artificial features and deep learnings.

关 键 词：隐喻识别 名词隐喻 语义理解 Transformer模型 BERT模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]