Information extraction meets relation databases
Bibliographic record
Abstract
Information extraction from unstructured text has much in common with querying in databases systems. Despite some differences on how data is modeled or represented, the general goal remains the same, i.e. to retrieve data or tag elements that satisfy some user-specified constraints. In recent years, the two paradigms have become much closer thanks to the large volume of data on the World Wide Web and the need for more automated search tools for information extraction and often the need for relating the extracted pieces. Several developments have contributed to the growth of the area including the work on named entity recognition (marked by MUC-6 and subsequent conferences) and natural language processing, Web information retrieval and mining, and Web query languages inspired by the query languages in the relational world. This panel explores the areas where the two paradigms overlap, the impacts and contributions they have had on each other and the areas that may be open for further research. The panel will bring together researchers who have worked in some established areas that closely relate to extracting structured information from unstructured text. In the first (role-playing) round, each panelist will strongly take a side on where the intersection is heading, arguing that one area will subsume the other area in near future. In the second round, the panelists will counter one or two others, pointing out the challenges that one area would be facing in subsuming the other and implications for future research directions.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.014 | 0.042 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.034 |
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".