Extrapolating human judgments from skip-gram vector representations of word meaning

ABSTRACT

There is a growing body of research in psychology that attempts to extrapolate human lexical judgments from computational models of semantics. This research can be used to help develop comprehensive norm sets for experimental research, it has applications to large-scale statistical modelling of lexical access and has broad value within natural language processing and sentiment analysis. However, the value of extrapolated human judgments has recently been questioned within psychological research. Of primary concern is the fact that extrapolated judgments may not share the same pattern of statistical relationship with lexical and semantic variables as do actual human judgments; often the error component in extrapolated judgments is not psychologically inert, making such judgments problematic to use for psychological research. We present a new methodology for extrapolating human judgments that partially addresses prior concerns of validity. We use this methodology to extrapolate human judgments of valence, arousal, dominance, and concreteness for 78,286 words. We also provide resources for users to extrapolate these human judgments for three million English words and short phrases. Applications for large sets of extrapolated human judgments are demonstrated and discussed.

KEYWORDS:

• Affect
• Co-occurrence models
• Human judgment
• Semantics
• Skip-gram
• Word2vec

Acknowledgements

We thank two anonymous reviewers for helpful advice on an earlier draft of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCiD

Geoff Hollis http://orcid.org/0000-0002-8922-1613