结合强化学习和DenseNet的远程监督关系抽取模型  

DISTANT SUPERVISION RELATION EXTRACTION COMBINING REINFORCEMENT LEARNING AND DENSENET

作　　者：冯轩闻 袁新瑞[1] 孙霞[2] 高厦 Feng Xuanwen;Yuan Xinrui;Sun Xia;Gao Sha(Network and Data Center,Northwest University,Xi’an 710127,Shaanxi,China;School of Information Science&Technology,Northwest University,Xi’an 710127,Shaanxi,China)

机构地区：[1]西北大学网络和数据中心,陕西西安710127 [2]西北大学信息科学与技术学院,陕西西安710127

出　　处：《计算机应用与软件》2024年第2期138-144,208,共8页Computer Applications and Software

基　　金：国家自然科学基金项目(61877050)。

摘　　要：关系抽取是信息获取领域的重要任务之一。为了更好地解决数据集中的噪声问题和句子深层次语义表征,提出一种结合强化学习和密集连接卷积神经网络的远程监督关系抽取模型,模型分为句子选择器和关系分类器。在句子选择器中,基于强化学习的方法能有效过滤噪声语句,提升输入数据质量;在关系分类器中,通过DenseNet深层网络中的特征复用,学习更丰富的语义特征。在NYT数据集上的实验结果表明句子选择器能够有效过滤噪声,该模型的关系抽取性能相比基线模型得到有效提高。Relation extraction is an important task in the field of information extraction.In order to better solve the noise problem and deep semantic representation of sentences in the dataset,a distant supervision relation extraction model combining reinforcement learning and densely connected convolutional networks(DenseNet)is proposed,which is divided into sentence selector and relation classifier.In the sentence selector,the reinforcement learning method could search for noisy sentences and effectively improve the quality of input data.In the relation classifier,DenseNet could realize feature reuse and learn richer semantic features.The experimental results on the NYT dataset prove that the sentence selector can effectively filter noise,and the relation extraction performance of the model is better than the baseline model.

关 键 词：关系抽取 远程监督 强化学习 卷积神经网络 密集连接 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP3[自动化与计算机技术—控制科学与工程]