TEI Lex-0 In Action: Improving the Encoding of the Dictionary of the Academia das Ciências de Lisboa
Bibliographic record
Abstract
This paper describes some experiments made while encoding the first complete dictionary of the Academia das Ciências de Lisboa (DACL) in the context of TEI Lex-0, a community-based interchange format for lexical data aimed at facilitating the interoperability and reusability of lexical resources. Even though the original encoding of the DACL was based on TEI, we decided to switch to TEI Lex-0 because it allowed us to streamline our encoding. Our experiments show that even though TEI Lex-0 is stricter than TEI itself (allowing fewer elements and imposing certain constraints that are not present in plain TEI), it is fully capable of representing the complexities of the entry structure of the DACL. In the paper, we discuss the TEI Lex-0 encoding of the DACL, as well as the conversion methodology and the tools used for the automatic conversion from the original encoding. We are currently focusing on the macrostructural level, more precisely on the types of lexical units and on the written and spoken forms of the lemma, providing a set of modelling principles and representation forms of every type of entry in the DACL. This paper is part of ongoing work and a contribution to the efforts of the DARIAH-ERIC Lexical Resources working group.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".