Bibliographic record
Abstract
Presentational XML documents, such as XHTML or Presentation MathML, use XML tags mainly for formating purposes, while descriptive XML applications, such as a wellstructured movie database, use tags to structure data items in a semantically meaningful way. There is little semantic connection between tags in a presentational XML document and its content, so the tagging is often complex and seemingly ambiguous. These differences make inference of the underlying structure more difficult for presentational XML. The problem of schema or grammar inference has been studied mostly for descriptive XML, and proposed solutions are often ineffective for presentational XML. On the other hand, there are many applications such as data extraction tools and special-purpose search engines that need to infer structure from presentational XML. Current proposals for such systems provide only partial solutions to this problem. Restrictions imposed by DTDs and XML Schemas make them insufficient to describe many presentational XML documents effectively. In this paper we use regular tree grammars to define a class of grammars that is able to model many published presentational XML documents. We also propose an algorithm to infer such grammars, and prove that we can infer an appropriate grammar with high probability from given samples. We also empirically evaluate our algorithm by applying it to various types of presentational XML and comparing it to other algorithms. 1.
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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".