Discovering and organizing noun-verb collocations in specialized corpora using inductive logic programming
Bibliographic record
Abstract
This article presents a method for discovering and organizing noun-verb (N-V) combinations found in a French corpus on computing. Our aim is to find N-V combinations in which verbs convey a “realization meaning” as defined in the framework of lexical functions (Mel’čuk 1996, 1998). Our approach, chiefly corpus-based, uses a machine learning technique, namely Inductive Logic Programming (ILP). The whole acquisition process is divided into three steps: (1) isolating contexts in which specific N-V pairs occur; (2) inferring linguistically-motivated rules that reflect the behaviour of realization N-V pairs; (3) projecting these rules on corpora to find other valid N-V pairs. This technique is evaluated in terms of the relevance of the rules inferred and in terms of the quality (recall and precision) of the results. Results obtained show that our approach is able to find these very specific semantic relationships (the realization N-V pairs) with very good success rates.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".