摘要
关系抽取旨在从句子中识别出实体对之间的关系类型。在关系抽取领域,目前主流的方法都使用了深度学习方法,但大部分方法在输入层没有对词向量进行深层次的讨论。针对这一不足,提出了一种基于多维语义映射的关系抽取方法,该方法的核心思想是将矩阵降维方法应用于神经网络模型输入层。通过将表示文本的词向量进行多维度的降维分解,使分解后的词向量能映射表示同一语句在不同维度上的语义信息。实验结果表明,在Chinese Literature Text和SemEval-2010 Task8数据集上F1值分别达到了75.3%和88.9%,验证了所提方法的有效性。
Relation extraction aims to identify relation types between entities from texts.In the field of relation extraction,most of existing methods use deep learning methods,but they do not have in-depth discussion of word vectors in the input layer.To further exploit word vectors,this paper proposes a relation extraction method based on multi-dimensional semantic mapping.The core idea of the method is to reduce dimensionality of text feature matrix before the word vector enters the input layer.Experimental results show that the proposed method not only can reduce dimensionality effectively,but also can represent the semantic information of the same sentence in different dimensions,with its F1 of 75.3%and 88.9%on the Chinese Literature Text and SemEval-2010 Task8 datasets,respectively.
作者
程华龄
陈艳平
杨卫哲
秦永彬
黄瑞章
CHENG Hua-ling;CHEN Yan-ping;YANG Wei-zhe;QIN Yong-bin;HUANG Rui-zhang(College of Computer Science and Technology,Guizhou University,Guiyang,550025,China;State Key Laboratory of Public Big Data,College of Computer Science and Technology,Guizhou University,Guiyang 550025,China)
出处
《计算机科学》
CSCD
北大核心
2022年第11期206-211,共6页
Computer Science
基金
国家自然科学基金通用联合基金(U1836205)
国家自然科学基金(62066007,62066008)
贵州省科学技术基金重点(黔科合基础[2020]1Z055)。
关键词
关系抽取
神经网络
多维映射
语义信息
Relation extraction
Neural network
Multidimensional mapping
Semantic information
