Recognizing Biomedical Named Entities in the Absence of Human Annotated Corpora
Bibliographic record
Abstract
Biomedical named entity recognition is an important task in biomedical text mining. Currently the dominant approach is supervised learning, which requires a sufficiently large human annotated corpus for training. In this paper, we propose a novel approach aimed at minimizing the annotation requirement. The idea is to use a dictionary which is essentially a list of entity names compiled by domain experts and sometimes more readily available than domain experts themselves. Given an unlabelled training corpus, we label the sentences by a simple dictionary lookup, which provides us with highly reliable but incomplete positive data. We then run a SVM-based self-training process in the spirit of semi-supervised learning to iteratively learn from the positive and unlabelled data to build a reliable classifier. Our evaluation on the BioNLP-2004 shared task data sets suggests that the proposed method can be a feasible alternative to traditional approaches when human annotation is not available.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".