Piecewise convolutional neural network relation extraction with self-attention mechanism

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pattern Recognition Pub Date : 2024-10-18 DOI:10.1016/j.patcog.2024.111083
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引用次数: 0

Abstract

The task of relation extraction in natural language processing is to identify the relation between two specified entities in a sentence. However, the existing model methods do not fully utilize the word feature information and pay little attention to the influence degree of the relative relation extraction results of each word. In order to address the aforementioned issues, we propose a relation extraction method based on self-attention mechanism (SPCNN-VAE) to solve the above problems. First, we use a multi-head self-attention mechanism to process word vectors and generate sentence feature vector representations, which can be used to extract semantic dependencies between words in sentences. Then, we introduce the word position to combine the sentence feature representation with the position feature representation of words to form the input representation of piecewise convolutional neural network (PCNN). Furthermore, to identify the word feature information that is most useful for relation extraction, an attention-based pooling operation is employed to capture key convolutional features and classify the feature vectors. Finally, regularization is performed by a variational autoencoder (VAE) to enhance the encoding ability of model word information features. The performance analysis is performed on SemEval 2010 task 8, and the experimental results show that the proposed relation extraction model is effective and outperforms some competitive baselines.
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具有自我关注机制的片断卷积神经网络关系提取
自然语言处理中关系提取的任务是识别句子中两个指定实体之间的关系。然而,现有的模型方法没有充分利用词的特征信息,也很少关注每个词的相对关系提取结果的影响程度。针对上述问题,我们提出了一种基于自注意机制的关系提取方法(SPCNN-VAE)来解决上述问题。首先,我们使用多头自注意机制处理词向量,生成句子特征向量表示,用于提取句子中词与词之间的语义依赖关系。然后,我们引入词的位置,将句子特征表示与词的位置特征表示相结合,形成片断卷积神经网络(PCNN)的输入表示。此外,为了识别对关系提取最有用的单词特征信息,我们采用了基于注意力的池化操作来捕捉关键卷积特征并对特征向量进行分类。最后,通过变异自动编码器(VAE)进行正则化,以增强模型词信息特征的编码能力。在 SemEval 2010 任务 8 中进行了性能分析,实验结果表明所提出的关系提取模型是有效的,其性能优于一些竞争基线。
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来源期刊
Pattern Recognition
Pattern Recognition 工程技术-工程:电子与电气
CiteScore
14.40
自引率
16.20%
发文量
683
审稿时长
5.6 months
期刊介绍: The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.
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