An Application of Operational Research to Computational Linguistics: Word Ambiguity
Bibliographic record
Abstract
This paper draws on graph theory and optimization techniques to develop a new measure of word ambiguity (e.g., homonymy and polysemy) for use in psycholinguistic research. This measure provides information regarding the uncertainty of the intended meaning of English words. Specifically, data about fifty thousand distinct words was collected from a corpus of close to six hundred million words. These data are used to generate information about word association which forms a basis for the creation of semantic graphs from which clusters are created and analyzed. The clusters identify groups of words related to the different meanings of a word and are used to calculate a set of relative probabilities for the meanings. These are in turn used to calculate the information entropy for the word, which acts as a surrogate measure of ambiguity. A genetic algorithm is used to optimally determine parameters for our formula for word association and for the graph clustering algorithm. The effectiveness of this application is demonstrated with examples from psycholinguistic research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".