A hybrid approach to the identification and expansion of abbreviations
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
This paper introduces a two-stage system for identifying and expanding abbreviations. It is based on a hybrid architecture where rule-based and statistical methods are combined. The first task of the system is to differentiate abbreviations from other types of unknown words such as names and misspellings. The second task of the system is to identify the intended complete word. The system is evaluated using data from the Air Safety Reporting System (ASRS) database: a domain where document retrieval is directly impacted by the large amount of unknown words, of which abbreviations are a frequent class. Introduction Natural language text is not always ideal for information retrieval (IR). Many information-rich documents contain misspellings, abbreviations, and other misleading variants of the key words that are necessary for quality information retrieval. For example, retrieval of records from the Air Safety Reporting System (ASRS) database is complicated by the fact that approximately e...
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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