GeoNER: Geological Named Entity Recognition with Enriched Domain Pre‐Training Model and Adversarial Training
Bibliographic record
Abstract
Abstract As important geological data, a geological report contains rich expert and geological knowledge, but the challenge facing current research into geological knowledge extraction and mining is how to render accurate understanding of geological reports guided by domain knowledge. While generic named entity recognition models/tools can be utilized for the processing of geoscience reports/documents, their effectiveness is hampered by a dearth of domain‐specific knowledge, which in turn leads to a pronounced decline in recognition accuracy. This study summarizes six types of typical geological entities, with reference to the ontological system of geological domains and builds a high quality corpus for the task of geological named entity recognition (GNER). In addition, GeoWoBERT‐advBGP (Geological Word‐base BERT‐adversarial training Bi‐directional Long Short‐Term Memory Global Pointer) is proposed to address the issues of ambiguity, diversity and nested entities for the geological entities. The model first uses the fine‐tuned word granularity‐based pre‐training model GeoWoBERT (Geological Word‐base BERT) and combines the text features that are extracted using the BiLSTM (Bi‐directional Long Short‐Term Memory), followed by an adversarial training algorithm to improve the robustness of the model and enhance its resistance to interference, the decoding finally being performed using a global association pointer algorithm. The experimental results show that the proposed model for the constructed dataset achieves high performance and is capable of mining the rich geological information.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".