Term generalization and synonym resolution for biological abstracts
Bibliographic record
Abstract
The field of molecular biology is growing at an astounding rate and research findings are being deposited into public databases, such as Swiss-Prot. Many of the over 200,000 protein entries in Swiss-Prot 49.1 lack annotations such as subcellular localization or function, but the vast majority have references to journal abstracts describing related research. These abstracts represent a huge amount of information that could be used to generate annotations for proteins automatically. Training classifiers to perform text categorization on abstracts is one way to accomplish this task. We present a method for improving text classification for biological journal abstracts by generating additional text features using the knowledge represented in a biological concept hierarchy (the Gene Ontology). The structure of the ontology, as well as the synonyms recorded in it, are leveraged by our simple technique to significantly improve the F-measure of subcellular localization text classifiers by as much as 0.078 and we achieve F-measures as high as 0.935.
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.
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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".