计算机科学 ›› 2022, Vol. 49 ›› Issue (5): 200-205.doi: 10.11896/jsjkx.210300198
陆亮, 孔芳
LU Liang, KONG Fang
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。
中图分类号:
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