Annotation of Financial Entities Using A Comprehensive Scheme in Turkish
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Information extraction (IE) which refers to the task of turning texts into structured form is also employed in finance domain for extraction of information which have a big importance for different financial concepts such as market, stock, and indices etc. As many other applications in Natural Language Processing(NLP), annotated corpora which involves entities, that represent characteristics of the related domain, is also essential resources for training and evaluation of IE models. Unfortunately, the creation of these resources is rather thorny, thus the scarcity of annotated language resources is one of the most prominent problems for lesser-studied language; as in the case for Turkish. In this paper, we present an ontology of financial concepts, and an effort to produce a high-quality corpus which includes 500 news documents annotated with these concepts in Turkish. We employ the dataset in the training of a baseline entity recognition model, and performance achieved over the dataset is 64.5% F-scores.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 it