Similarity spaces are standardly constructed by collecting pairwise similarity judgments and subjecting those to a dimension-reduction technique such as multidimensional scaling or principal component analysis. While this approach can be effective, it has some known downsides, most notably, it tends to be costly and has limited generalizability. Recently, a number of authors have attempted to mitigate these issues through machine learning techniques. For instance, neural networks have been trained on human similarity judgments to infer the spatial representation of unseen stimuli. However, these newer methods are still costly and fail to generalize widely beyond their initial training sets. This paper proposes leveraging prebuilt semantic vector spaces as a cheap alternative to collecting similarity judgments. Our results suggest that some of those spaces can be used to approximate human similarity judgments at low cost and high speed.
NETL
NSTL
Springer Nature
