论文标题
量子启发的单词表示和计算
Quantum Inspired Word Representation and Computation
论文作者
论文摘要
单词含义具有不同的方面，而现有单词表示将这些方面“压缩”到单个向量中，并且需要进一步的分析才能在不同的维度中恢复信息。受量子概率的启发，我们将单词表示为密度矩阵，它们本质上能够表示混合状态。该实验表明，密度矩阵表示形式可以有效地捕获单词含义的不同方面，同时保持与矢量表示的可比较可靠性。此外，我们提出了一种新的方法，以结合矢量和密度矩阵的计算中的连贯求和和不一致的求和。它在单词类比任务上取得了一致的改进。
Word meaning has different aspects, while the existing word representation "compresses" these aspects into a single vector, and it needs further analysis to recover the information in different dimensions. Inspired by quantum probability, we represent words as density matrices, which are inherently capable of representing mixed states. The experiment shows that the density matrix representation can effectively capture different aspects of word meaning while maintaining comparable reliability with the vector representation. Furthermore, we propose a novel method to combine the coherent summation and incoherent summation in the computation of both vectors and density matrices. It achieves consistent improvement on word analogy task.
