Context Matters: Recovering Human Semantic Structure from Machine Learning Analysis of Large-Scale Text Corpora.

ABSTRACT: Applying machine learning algorithms to automatically infer relationships between concepts from large-scale collections of documents presents a unique opportunity to investigate at scale how human semantic knowledge is organized, how people use it to make fundamental judgments ("How similar are cats and bears?"), and how these judgments depend on the features that describe concepts (e.g., size, furriness). However, efforts to date have exhibited a substantial discrepancy between algorithm predictions and human empirical judgments. Here, we introduce a novel approach to generating embeddings for this purpose motivated by the idea that semantic context plays a critical role in human judgment. We leverage this idea by constraining the topic or domain from which documents used for generating e

SUBMITTER: Iordan MC 

PROVIDER: S-EPMC9285590 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature