Functional Distributional Semantics (FDS) models the meaning of words by truth-conditional functions. This provides a natural representation for hypernymy, but no guarantee that it is learnt when FDS models are trained on a corpus. We demonstrate that FDS models learn hypernymy when a corpus strictly follows the Distributional Inclusion Hypothesis. We further introduce a training objective that allows FDS to handle simple universal quantifications, thus enabling hypernymy learning under the reverse of DIH. Experimental results on both synthetic and real data sets confirm our hypotheses and the effectiveness of our proposed objective.

翻译：功能分布语义学通过真值条件函数建模词语意义。这为上下位关系提供了自然表征，但无法保证功能分布语义模型在语料库训练时能习得该关系。我们证明，当语料严格遵循分布包含假设时，功能分布语义模型能习得上下位关系。我们进一步引入一种训练目标，使功能分布语义能够处理简单的全称量化，从而在反转的分布包含假设条件下实现上下位关系学习。基于合成数据集与真实数据集的实验结果均验证了我们的假设及所提目标的有效性。