NeRBERT- A Biomedical Named Entity Recognition Tagger
Bibliographic record
Abstract
Biomedical named entity recognition is a popular research topic in the Biosciences domain as number of biomedical articles getting published are increasing rapidly.Generic models using machine learning and deep learning techniques have been proposed for extracting these entities in the past, however there is no clear verdict on which techniques are better and how these generic models perform in a domain-specific big data scenario.In this paper, we evaluate three baseline models using the most complex BioNLP 2013 cancer genetics dataset addressing the cancer domain.A classifier ensemble, bidirectional long short-term memory (Bi-LSTM) model and the bidirectional encoder representations from transformers (BERT) model are implemented.We propose NeRBERT, a domain-specific, graphical processing unit (GPU) pre-trained language model using extra biomedical corpora extending BERTBASE.Experimental results prove the efficacy of NeRBERT as it outperforms the other three models with an F1-score gain of 12.18 pp, 8.59 pp and 5.43 pp over the ensemble, Bi-LSTM and BERT models respectively.GPUs reduce the model training time to less than half.Comparing it to existing state-of-the-art models, it performs 1.57 pp higher than the next best existing model compared, emerging as a robust biomedical and cancer phenotyping NER tagger.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.030 |
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; both teacher heads agree on what is shown here.
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".