NRC-Canada at SMM4H Shared Task: Classifying Tweets Mentioning Adverse\n Drug Reactions and Medication Intake
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
Our team, NRC-Canada, participated in two shared tasks at the AMIA-2017\nWorkshop on Social Media Mining for Health Applications (SMM4H): Task 1 -\nclassification of tweets mentioning adverse drug reactions, and Task 2 -\nclassification of tweets describing personal medication intake. For both tasks,\nwe trained Support Vector Machine classifiers using a variety of surface-form,\nsentiment, and domain-specific features. With nine teams participating in each\ntask, our submissions ranked first on Task 1 and third on Task 2. Handling\nconsiderable class imbalance proved crucial for Task 1. We applied an\nunder-sampling technique to reduce class imbalance (from about 1:10 to 1:2).\nStandard n-gram features, n-grams generalized over domain terms, as well as\ngeneral-domain and domain-specific word embeddings had a substantial impact on\nthe overall performance in both tasks. On the other hand, including sentiment\nlexicon features did not result in any improvement.\n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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