  Applying machine learning algorithms to large-scale, text-based corpora
(embeddings) presents a unique opportunity to investigate at scale how human
semantic knowledge is organized and how people use it to judge fundamental
relationships, such as similarity between concepts. However, efforts to date
have shown a substantial discrepancy between algorithm predictions and
empirical judgments. Here, we introduce a novel approach of generating
embeddings motivated by the psychological theory that semantic context plays a
critical role in human judgments. Specifically, we train state-of-the-art
machine learning algorithms using contextually-constrained text corpora and
show that this greatly improves predictions of similarity judgments and feature
ratings. By improving the correspondence between representations derived using
embeddings generated by machine learning methods and empirical measurements of
human judgments, the approach we describe helps advance the use of large-scale
text corpora to understand the structure of human semantic representations.
