论文标题
从意义到感知 - 探索单词和气味感知嵌入之间的空间
From meaning to perception -- exploring the space between word and odor perception embeddings
论文作者
论文摘要
在本文中，我们提出了使用词2VEC算法的使用，以获取气味感知的嵌入（或气味嵌入），仅使用公开可用的香水描述。除了在彼此之间显示出有意义的相似性关系外，这些嵌入还证明具有与它们各自的单词嵌入的共同信息。这些嵌入的意义表明，美学可能会提供足够的约束，以使用由非随机组合数据的分布语义动机的算法。此外，它们为对气味进行分类和分析香水的新方法提供了可能性。我们还采用了嵌入，以基于真实和随机生成的香水之间的差异来理解香水的美学性质。在另一个暂定实验中，我们探讨了单词嵌入空间与气味感知嵌入空间之间映射的可能性，通过将回归器拟合在共享词汇量上，然后预测单词的气味感知嵌入而没有先验相关的气味，例如夜间或天空。
In this paper we propose the use of the Word2vec algorithm in order to obtain odor perception embeddings (or smell embeddings), only using publicly available perfume descriptions. Besides showing meaningful similarity relationships among each other, these embeddings also demonstrate to possess some shared information with their respective word embeddings. The meaningfulness of these embeddings suggests that aesthetics might provide enough constraints for using algorithms motivated by distributional semantics on non-randomly combined data. Furthermore, they provide possibilities for new ways of classifying odors and analyzing perfumes. We have also employed the embeddings in an attempt to understand the aesthetic nature of perfumes, based on the difference between real and randomly generated perfumes. In an additional tentative experiment we explore the possibility of a mapping between the word embedding space and the odor perception embedding space by fitting a regressor on the shared vocabulary and then predict the odor perception embeddings of words without an a priori associated smell, such as night or sky.
