NRC-Canada at SMM4H Shared Task: Classifying Tweets Mentioning Adverse\n Drug Reactions and Medication Intake
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
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.025 |
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".