MARTT: Using induced knowledge base to automatically mark up plant taxonomic descriptions with XML
Bibliographic record
Abstract
Abstract Despite the sub‐language nature of taxonomic descriptions of plants, researchers warned about the large variations among different collections of descriptions in terms of information contents and presentations. These variations impose a serious challenge to the development of automatic tools for the semantic markup of large volumes of free‐text descriptions. This paper presents a new approach to automatic markup of multiple collections of taxonomic descriptions with XML. The effectiveness of the approach was demonstrated with markup experiments using three contemporary floras. The markup system, MARTT, was based on supervised machine learning algorithms and enhanced by machine learned association rules representing certain types of domain knowledge and conventions. Experiments showed that our simple and efficient markup algorithm outperformed popular general‐purpose algorithms (including SVMs) across different floras. More importantly, the results demonstrated that the domain knowledge learned from one flora was useful for improving the markup performance on a second flora, especially on elements with sparse training examples. The system design and the evaluation of markup algorithms are reported in this paper. The study on the effectiveness of the induced knowledge base will be reported in a later paper. In this paper, common practices of flora authors and the potentials of MARTT system for improving the efficiency and effectiveness of the creation, organization, and utilization of plant descriptions are also discussed.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".