计算机科学 ›› 2019, Vol. 46 ›› Issue (10): 215-221.doi: 10.11896/jsjkx.190400071
罗达, 苏锦钿, 李鹏飞
LUO Da, SU Jin-dian, LI Peng-fei
摘要: 近年来,基于知识库的问答受到了广泛的关注,成为了一个重要的自然语言处理任务。在基于知识库的问答任务中,简单问题是指能够通过知识库的单一事实进行回答的问题。针对简单问题的回答,现有的解决方法主要是将问题和知识库事实映射到同一向量空间中,然后通过计算问题和事实之间的相似度来得到答案,但这种方法会损失原始单词的部分语义交互信息。针对该问题,文中提出了一种基于多角度注意力机制的关系检测模型,从多个角度对问题和知识库关系的相关性进行了建模,从而保留了更多的原始交互信息,并提高了模型的准确率。此外,为了减小噪音的影响并提高实体识别的准确率,在实体链接过程中提出结合基于语言模型的动态词向量和单词的词性特征对问题进行表征。实验结果表明,所提方法在基于FB2M和FB5M的SimpleQuestions数据集上分别获得了78.9%和78.3%的准确率,能够很好地反映问题与知识库关系之间的语义相关性,并提升了单一事实知识库问答的准确率。
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