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Simple Co-Occurrence Statistics Reproducibly Predict Association Ratings.

Markus J Hofmann¹, Chris Biemann², Chris Westbury³

¹Department of Psychology, University of Wuppertal.

Cognitive Science

|August 12, 2018

Summary

Human word association ratings are best explained by association strength (AS) and semantic similarity measures. These factors consistently predict how strongly people link words together, outperforming other linguistic and emotional features.

Keywords:

Association strength Associative read-out model Co-occurrence statistics Interactive activation model Semantic long-term memory

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Area of Science:

Cognitive Psychology
Computational Linguistics
Psycholinguistics

Background:

Understanding human word association is crucial for cognitive and linguistic research.
Previous studies explored various factors like word frequency, emotional valence, arousal, and imageability.
The predictive power of different association measures in human ratings requires further investigation.

Purpose of the Study:

To determine the key factors influencing human ratings of word association strength.
To test the efficacy of association strength (AS) derived from co-occurrence statistics.
To compare AS with other linguistic features and semantic similarity measures.

Main Methods:

Three studies were conducted using linear mixed effects models.
Hierarchical cluster analysis was employed to group related predictors.
Word association strength (AS) and word2vec skip-gram cosine distances were primary measures.

Main Results:

Association strength (AS) and emotional valence reproducibly accounted for variance in human ratings.
Only AS and word2vec skip-gram cosine distances consistently predicted ratings across all studies.
Other predictors, including word frequency, arousal, imageability, and PMI, showed limited or inconsistent predictive power.

Conclusions:

Association strength (AS) and paradigmatic semantic similarity (skip-gram cosine) offer the most robust explanation for human association ratings.
Emotional valence plays a significant role in semantic space perception.
These findings highlight the importance of both contiguity and similarity in understanding word associations.