计算机科学 ›› 2020, Vol. 47 ›› Issue (8): 164-170.doi: 10.11896/jsjkx.190600153
赵威1, 2, 林煜明1, 王超强1, 蔡国永1
ZHAO Wei1, 2, LIN Yu-ming1, WANG Chao-qiang1, CAI Guo-yong1
摘要: 同一类商品下, 观点词对中包含的观点目标和观点词通常有着很强的观点依赖联系, 因此可以通过对评论句子中单词间的观点依赖联系进行分析来提取观点词对。首先, 构建评论句子的依赖联系分析模型来获取评论句子中每个单词之间的依赖联系信息, 文中选择的基本模型是LSTM神经网络;然后, 假设评论句子中所包含的观点词对中的一项是已知的, 并将该已知项作为模型的注意力信息, 使得模型能够从评论句子中有重点地提取出与该已知项具有强观点依赖联系的单词或词组, 并将其作为观点词对中的另一未知项;最后, 将观点依赖联系得分最高的词对作为观点词对并输出。文中进一步设计了一种复合模型, 通过结合两种包含不同已知项信息的上述模型, 来实现在不需要提前知道已知项的情况下观点词对的挖掘。
中图分类号:
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