Bibliographic record
Abstract
In this paper we will present a simple, yet effective, method for extracting terminology from technical text. The method is based on the observation that for technical domains it is much simpler to describe what a valid terminological unit cannot be than what it can possibly be. Our method relies on a set of filters that exclude multi-word units according to simple rules regarding their context and internal lexical structure, and it does not require any special pre-processing such as POS tagging. Rules were hand-coded in a simple incremental process and may be ported to several languages with little effort. Additionally, the method is able to process more than two million words per minute on a standard computer. Although the method was originally intended for semiautomatic terminological extraction, we believe that it can also be applied in fully automated procedures, making it appropriate for large-scale information extraction. We will start by explaining our main motivation for building this method and we will describe its role in a larger framework, the Corpógrafo. We will then present the process of building the current method, from the first very simple approaches to the current version, pointing out the problems encountered at each step. We will then present results of applying the current version of the extraction method to specific domain corpora in English. Finally, we will present future plans and explain how we are currently in the process of building a small semantic lexicon for helping future large-scale information extraction procedures.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.016 |
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".