GraphNER: Using Corpus Level Similarities and Graph Propagation for Named Entity Recognition
Bibliographic record
Abstract
The rapidly growing amount of research papers in computational biology makes it difficult for researchers to keep up to date on new results. The motivation behind this paper is to use natural language processing to automatically understand relevant concepts from the large amount of text data in published papers in computational biology. As a proof-of-concept, we focus on the gene mention detection task, which allows us to identify genes that are being discussed in papers, making it possible to search for concepts like genes rather than searching on words. In this paper we introduce GraphNER, a semi-supervised machine learning model for named entity recognition (NER). In particular, we use GraphNER to identify gene mentions in natural language data such as biomedical papers. It combines training data where the gene mentions are identified by human experts and unlabelled data that contains many other relevant gene mentions. The labeled and unlabeled data are linked together using similarities between n-grams that occur in the two data sources (an n-gram is a contiguous sequence of n words in the text). GraphNER uses the information gleaned from this graph, and combines it with a conditional random field (CRF) model for NER. We consider two different CRF-based NER systems on two different datasets combined with our graph model for semi-supervised learning for the task of gene mention detection. We show that GraphNER consistently improves the overall quality of gene mention detection due to its higher precision. GraphNER is freely available at http://www.bcgsc.ca/platform/ bioinfo/software/graphner.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".