基于掩码提示与门控记忆网络校准的关系抽取方法  

Relation extraction method based on mask prompt and gated memory network calibration

作　　者：魏超 陈艳平[1,2,3] 王凯 秦永彬[1,2,3] 黄瑞章[1,2,3] WEI Chao;CHEN Yanping;WANG Kai;QIN Yongbin;HUANG Ruizhang(Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry(Guizhou University),Guiyang Guizhou 550025,China;State Key Laboratory of Public Big Data(Guizhou University),Guiyang Guizhou 550025,China;College of Computer Science and Technology,Guizhou University,Guiyang Guizhou 550025,China)

机构地区：[1]文本计算与认知智能教育部工程研究中心(贵州大学),贵阳550025 [2]公共大数据国家重点实验室(贵州大学),贵阳550025 [3]贵州大学计算机科学与技术学院,贵阳550025

出　　处：《计算机应用》2024年第6期1713-1719,共7页journal of Computer Applications

基　　金：国家自然科学基金资助项目(62166007)。

摘　　要：针对关系抽取(RE)任务中实体关系语义挖掘困难和预测关系有偏差等问题,提出一种基于掩码提示与门控记忆网络校准(MGMNC)的RE方法。首先,利用提示中的掩码学习实体之间在预训练语言模型(PLM)语义空间中的潜在语义,通过构造掩码注意力权重矩阵,将离散的掩码语义空间相互关联;其次,采用门控校准网络将含有实体和关系语义的掩码表示融入句子的全局语义;再次,将它们作为关系提示校准关系信息,随后将句子表示的最终表示映射至相应的关系类别;最后,通过更好地利用提示中掩码,并结合传统微调方法的学习句子全局语义的优势,充分激发PLM的潜力。实验结果表明,所提方法在SemEval(SemEval-2010 Task 8)数据集的F1值达到91.4%,相较于RELA(Relation Extraction with Label Augmentation)生成式方法提高了1.0个百分点;在SciERC(Entities, Relations, and Coreference for Scientific knowledge graph construction)和CLTC(Chinese Literature Text Corpus)数据集上的F1值分别达到91.0%和82.8%。所提方法在上述3个数据集上均明显优于对比方法,验证了所提方法的有效性。相较于基于生成式的方法,所提方法实现了更优的抽取性能。To tackle the difficulty in semantic mining of entity relations and biased relation prediction in Relation Extraction(RE)tasks,a RE method based on Mask prompt and Gated Memory Network Calibration(MGMNC)was proposed.First,the latent semantics between entities within the Pre-trained Language Model(PLM)semantic space was learned through the utilization of masks in prompts.By constructing a mask attention weight matrix,the discrete masked semantic spaces were interconnected.Then,the gated calibration networks were used to integrate the masked representations containing entity and relation semantics into the global semantics of the sentence.Besides,these calibrated representations were served as prompts to adjust the relation information,and the final representation of the calibrated sentence was mapped to the corresponding relation class.Finally,the potential of PLM was fully exploited by the proposed approach through harnessing masks in prompts and combining them with the advantages of traditional fine-tuning methods.The experimental results highlight the effectiveness of the proposed method.On the SemEval(SemEval-2010 Task 8)dataset,the F1 score reached impressive 91.4%,outperforming the RELA(Relation Extraction with Label Augmentation)generative method by 1.0 percentage point.Additionally,the F1 scores on the SciERC(Entities,Relations,and Coreference for Scientific knowledge graph construction)and CLTC(Chinese Literature Text Corpus)datasets were remarkable,achieving 91.0%and 82.8%respectively.The effectiveness of the proposed method was evident as it consistently outperformed the comparative methods on all three datasets mentioned above.Furthermore,the proposed method achieved superior extraction performance compared to generative methods.

关 键 词：关系抽取 掩码 门控神经网络 预训练语言模型 提示学习 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]