Topic Modeling for Intellectual Property Research: Comparing Methods Through Simulation and Application
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Topic modeling is an increasingly popular component of intellectual property research.It has largely been applied to patent research; topic modeling research into trademarks is still in its infancy.Accordingly, there is no consensus among trademarks researchers regarding which topic modeling techniques lead to the best results.This thesis explores the applicability of several topic models to trademark text data.Several topic models are compared on the basis of the UMass coherence metric in simulated and real-data experiments.In simulations, models are evaluated on a series of increasingly sparse and complex synthetic corpora and their coherence scores are compared.Real-data experiments apply the models to a collection of trademark registrations filed with the Canadian Intellectual Property Office and review the coherence and contents of the learned topics.These experiments reveal that hierarchical network clustering methods are promising options for trademark topic modeling, establishing a baseline for future research.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it