计算机科学 ›› 2022, Vol. 49 ›› Issue (5): 200-205.doi: 10.11896/jsjkx.210300198

• 人工智能 • 上一篇    下一篇

面向对话的融入知识的实体关系抽取

陆亮, 孔芳   

  1. 苏州大学计算机科学与技术学院 江苏 苏州215006
  • 收稿日期:2021-03-19 修回日期:2021-07-16 出版日期:2022-05-15 发布日期:2022-05-06
  • 通讯作者: 孔芳(kongfang@suda.edu.cn)
  • 作者简介:(20185227045@stu.suda.edu.cn)
  • 基金资助:
    国家自然科学基金面上项目(61876118);国家自然基金重点项目(61836007)

Dialogue-based Entity Relation Extraction with Knowledge

LU Liang, KONG Fang   

  1. School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China
  • Received:2021-03-19 Revised:2021-07-16 Online:2022-05-15 Published:2022-05-06
  • About author:LU Liang,born in 1995,postgraduate,is a member of China Computer Federation.His main research interests include natural language processing and so on.
    KONG Fang,born in 1977,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.Her main research interests include natural language processing and discourse ana-lysis.
  • Supported by:
    General Program of National Natural Science Foundation of China(61876118) and Key Program of National Natural Foundation of China(61836007).

摘要: 实体关系抽取旨在从文本中抽取出实体之间的语义关系。该任务在新闻报道、维基百科等规范文本上的研究相对丰富,并取得了一定的成果,但面向对话文本的相关研究还处于起始阶段。目前用于实体关系抽取的对话语料规模较小且信息密度低,有效特征难以捕获;深度学习模型无法像人一样进行知识联想,单纯依靠加大标注数据量和增强计算力难以精细深度地理解对话内容。针对上述问题,提出了一个融入知识的实体关系抽取模型,使用Star-Transformer从对话文本中有效捕获特征,同时通过关键词共现的方式构建一个包含关系及其语义关键词的关系集合,将该集合与对话文本进行相关性计算后得到的重要关系特征作为知识融入模型中。在DialogRE公开数据集上进行实验,得到F1值为53.6%,F1c值为49.5%,证明了所提方法的有效性。

关键词: Transformer, 对话语境, 融入知识, 实体关系抽取, 注意力机制

Abstract: Entity relation extraction aims to extract semantic relations between entities from text.Up to now,related work on entity relation extraction mainly focuses on written texts,such as news and Wikipedia text,and has achieved considerable success.However,the research for dialogue texts is still in initial stage.At present,the dialogue corpus used for entity relation extraction is small in scale and low in information density,so it is difficult to capture effective features.The deep learning model does not associate knowledge like human beings,so it is difficult to understand the dialogue content in detail and depth simply by increasing the amount of annotation data and enhancing the computing power.In response to the above problems,this paper proposes a knowledge-integrated entity relation extraction model,which uses Star-Transformer to effectively capture features from dialogue texts,and constructs a relation set containing relations and their semantic keywords through the co-occurrence of keywords.The important relation features obtained by calculating the correlation between the set and dialogue text are integrated into the model as knowledge.Experiment results on the DialogRE dataset show that the F1 value is 53.6% and the F1c value is 49.5%,which proves the effectiveness of proposed method.

Key words: Attention mechanism, Dialogue context, Entity relation extraction, Integrate knowledge, Transformer

中图分类号: 

  • TP391
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