结合邻域知识的文档级关键词抽取方法  

Document-level Keyphrase Extraction Approach using Neighborhood Knowledge

作　　者：李晨亮 龙俊辉[1,2] 唐作立 周涛 LI Chen-liang;LONG Jun-hui;TANG Zuo-li;ZHOU Tao(Key Laboratory of Aerospace Information Security and Trusted Computing,Ministry of Education,Wuhan University,Wuhan,430072;School of Cyber Science and Engineering,Wuhan University,Wuhan,430072)

机构地区：[1]武汉大学空天信息安全与可信计算教育部重点实验室,武汉430072 [2]武汉大学国家网络安全学院,武汉430072

出　　处：《电子科技大学学报》2021年第4期551-557,共7页Journal of University of Electronic Science and Technology of China

基　　金：国家自然科学基金面上项目(61872278)。

摘　　要：基于编码器−解码器(encoder-decoder)框架的生成式方法在关键词抽取任务上得到了广泛应用并取得了较好的性能,然而该方法面临的主要挑战为建模有效的文档向量表示,及生成覆盖整个文档主题的关键词集合,这些挑战都会直接影响关键词抽取的结果。该文提出了结合邻域知识的文档级关键词抽取模型以应对这些挑战。具体来说,通过给指定文档添加少量的最近邻样本,原文档被扩展为一个文档集合。基于单词之间的距离将文档集合中的每个文档构建成词图,合并集合中的所有词图形成一个大图,然后利用图卷积网络进行编码。解码端引入了上下文修改机制和覆盖机制,使模型能够生成更加多样化的关键词来覆盖文档包含的所有主题。最后在4种数据集上分别与现有的基准模型进行对比,实验结果表明该方法能够有效提升关键词抽取的性能。Encoder-decoder-based generative approaches have been widely used and achieved good performance for keyphrase extraction tasks.However,the main challenges of the encoder-decoder-based approach are modeling an effective document vector representation and generating a set of keyphrases covering the entire document topic,which can directly affect the keyphrase extraction results.In this paper,a document-level keyphrase extraction model incorporating neighborhood knowledge is proposed to address the challenges mentioned above simultaneously.Specifically,the original document is extending to a document set by adding some nearest-neighbor documents.Then,each document in the set is constructed into a word graph based on the distance between words,and all the word graphs in the set are merged into a large graph,which is then encoded using graph convolutional networks.Besides,to fully cover diverse keyphrases and topics,the context modification mechanism and coverage mechanism are introduced at the decoding step.Finally,by comparing with the existing baseline model on four benchmark datasets,the experimental results show that the method can effectively improve the performance of extracting keyphrases.

关 键 词：深度学习 编码器−解码器框架 图卷积网络 图神经网络 关键词抽取 邻域知识 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]