Proceedings of the ACL 2014 Workshop on Semantic Parsing
Bibliographic record
Abstract
While there has been significant recent work on learning semantic parsers for specific task/ domains, the results don't transfer from one domain to another domains.We describe a project to learn a broad-coverage semantic lexicon for domain independent semantic parsing.The technique involves several bootstrapping steps starting from a semantic parser based on a modest-sized hand-built semantic lexicon.We demonstrate that the approach shows promise in building a semantic lexicon on the scale of WordNet, with more coverage and detail that currently available in widely-used resources such as VerbNet.We view having such a lexicon as a necessary prerequisite for any attempt at attaining broad-coverage semantic parsing in any domain.The approach we described applies to all word classes, but in this paper we focus here on verbs, which are the most critical phenomena facing semantic parsing.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.126 | 0.059 |
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".