Determining the Relativity of Word Meanings through the Construction of Individualized Models of Semantic Memory
Bibliographic record
Abstract
Distributional models of lexical semantics are capable of acquiring sophisticated representations of word meanings (Kumar, 2020). The main theoretical advantage of this approach is that the model’s demonstrate the systematic connection between the knowledge that people acquire and the experience that humans have with the natural language environment (Landauer & Dumais, 1997). However, linguistic experience is inherently variable and differs radically across people. Recently, Thompson, Roberts, and Lupyan (2020) used distributional modeling to examine how word meanings vary across languages and it was found that there is considerable variability in the meanings of words across languages for most semantic categories. The goal of this article is to examine how variable words meanings are across individual language users within a single language. This was accomplished by assembling 500 individual user corpora attained from the online forum Reddit (Baumgartner et al., 2020). Each user corpus ranged between 3.8 to 32.3 million words each, and a count-based distributional framework (Johns, Mewhort, & Jones, 2019; Johns, 2021) was used to extract word meanings for each user. These representations were then used to estimate the semantic alignment of word meanings across individual language users. It was found that there are significant levels of relativity in word meanings across individuals, and these differences are partially explained by other psycholinguistic factors, such as concreteness, semantic diversity, and social aspects of language usage. These results point to word meanings being fundamentally relative and fluid, with this relativeness being dependent on the individual nature of linguistic experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".