We propose a framework to model an operational conversational negation by
applying worldly context (prior knowledge) to logical negation in compositional
distributional semantics. Given a word, our framework can create its negation
that is similar to how humans perceive negation. The framework corrects logical
negation to weight meanings closer in the entailment hierarchy more than
meanings further apart. The proposed framework is flexible to accommodate
different choices of logical negations, compositions, and worldly context
generation. In particular, we propose and motivate a new logical negation using
matrix inverse.
We validate the sensibility of our conversational negation framework by
performing experiments, leveraging density matrices to encode graded entailment
information. We conclude that the combination of subtraction negation and
phaser in the basis of the negated word yields the highest Pearson correlation
of 0.635 with human ratings.

本文提出一个模型框架来模拟对话否定，该模型将世界上下文应用于逻辑否定，以组成分配语义。给定一个词，该框架可以创建一个类似于人类感知否定的否定。该框架可以纠正逻辑否定，并将意义更接近蕴含层次结构的意义重量相加，以容纳不同选择的逻辑否定，组合和世界性背景生成，尤其提出并激励了一个新的采用矩阵逆的逻辑否定，通过密度矩阵来编码分级蕴含信息，实验证明，采用减法否定和相移器的组合可以得到与人类评分最高的 Pearson 相关系数为 0.635。