Automatic Acquisition of Knowledge About Multiword Predicates
Bibliographic record
Abstract
Human interpretation of natural language relies heavily on cognitive processes involving metaphorical and idiomatic meanings.One area of computational linguistics in which such processes play an important, but largely unaddressed, role is the determination of the properties of multiword predicates (MWPs).MWPs such as give a groan and cut taxes involve metaphorical meaning extensions of highly frequent, and highly polysemous, verbs.Tools for automatically identifying such MWPs, and extracting their lexical and syntactic properties, are crucial to the adequate treatment of text in a computational system, due to the productive nature of MWPs across many languages.This paper gives an overview of our work addressing these issues.We begin by relating linguistic properties of metaphorical uses of verbs to their distributional properties.We devise automatic methods for assessing whether a verb phrase is literal, metaphorical, or idiomatic.Since metaphorical MWPs are generally semi-productive, we also develop computational measures of their individual acceptability and of their productivity over semantically related combinations.Our results demonstrate that combining statistical approaches with linguistic information is beneficial, both for the acquisition of knowledge about metaphorical and idiomatic MWPs, and for the organization of such knowledge in a computational lexicon.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".