结合百科知识与句子语义特征的关系抽取方法

doi:10.11896/JsJkx.190700042

摘要/Abstract

摘要： 关系抽取是信息抽取领域中重要的研究任务之一,其典型的应用场景包括知识图谱、问答系统、机器翻译等。目前已经有大量的研究工作将深度学习应用于关系抽取任务中,基于深度神经网络的关系抽取方法在很多场景中的表现都优于传统关系抽取方法。然而,目前基于深度神经网络的方法大多仅依赖于语料本身,缺乏外部知识的引入。针对这个问题,提出了一种结合百科知识与句子语义特征的神经网络关系抽取模型。该模型引入百科实体的背景描述信息作为外部知识,并通过注意力机制动态地从描述信息中提取实体特征,同时利用双向LSTM模型抽取句子中所包含的语义特征,最后结合实体特征和句子语义特征进行实体关系抽取。在人工标注数据集上的对比实验结果表明,文中所提模型的表现明显优于其他现有的关系抽取方法。

关键词: 长短期记忆网络, 实体关系抽取, 中文百科, 注意力机制

Abstract: Relation extraction is one of the important research topics in the field of information extraction.Its typical application scenarios include knowledge graphs,question answering systems,machine translation,etc.Recently,deep learning has been applied in a large amount of relation extraction researches,and deep neural networks based relationship extraction method performs much better than the traditional methods in many situations.However,most of the current deep neural network-based relation extraction methods Just rely on the corpus itself and lack the introduction of external knowledge.To address this issue,this paper proposed a neural network model,which combined encyclopedia knowledge and semantic features of sentences for relation extraction.The model introduced the description information of entities in encyclopedia as external knowledge,and dynamically extracted entity features through attention mechanism.Meanwhile,it employed bidirectional LSTM networks to extract the semantic features contained in the sentence.Finally,the model combined the entity features and the sentence semantic features for relation extraction.A series of experiments were carried out based on a manually labeled dataset.Experimental results demonstrate that the proposed model is superior to other existing relationship extraction methods.

Key words: Attention mechanism, Chinese encyclopedia, Entity relation extraction, Long short-term memory

中图分类号:

TP391

吕亿林, 田宏韬, 高建伟, 万怀宇. 结合百科知识与句子语义特征的关系抽取方法[J]. 计算机科学, 2020, 47(6A): 40-44. https://doi.org/10.11896/JsJkx.190700042

YU Yi-lin, TIAN Hong-tao, GAO Jian-wei and WAN Huai-yu. Relation Extraction Method Combining Encyclopedia Knowledge and Sentence Semantic Features[J]. Computer Science, 2020, 47(6A): 40-44. https://doi.org/10.11896/JsJkx.190700042

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