Dynamic Entity-Based Named Entity Recognition Under Unconstrained Tagging Schemes
Bibliographic record
Abstract
As increasingly more textual information becomes available, named entity recognition (NER) systems are thriving, benefiting from powerful models and expressive tagging schemes that promote the full use of diverse features at different levels. To improve performance, traditional approaches have focused mainly on changing the structures of NER models but have always ignored the hard constraints and left the NER tagging schemes unchanged. To solve this problem, this article proposes a dynamic entity-based NER approach under unconstrained tagging schemes. To eliminate the constraints, we reorganize widely used tagging schemes and propose two novel unconstrained schemes: one in which tags are assigned to words and entities separately, and one where words and entities are labeled indiscriminately by uniformly taking them as chunks. Associated with the unconstrained tagging schemes, two entity-based neural architectures are also presented that recognize entities at the same time that the sentence is dynamically segmented. Unlike other static NER models that process all the tags after labeling each word, our models address the inputs dynamically by the interactions between the input text and the output labels. The dynamic mechanism can ensure that the entity-level features are included in the NER system, which is helpful for correctly recognizing entities. Except for word embeddings pretrained from unlabeled corpora, no external language-specific knowledge or other resources such as gazetteers are used. The experiments with English, German, Dutch, and Spanish datasets show that our methods can perform very well with different languages. Particularly, the results of the recall rate against the entity’s length reveal that the proposed entity-based models are suitable for recognizing entities with long lengths.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".