Can Syntactic and Logical Graphs help Word Sense Disambiguation?
Bibliographic record
Abstract
This paper presents a word sense disambiguation (WSD) approach based on syntactic and logical representations.The objective here is to run a number of experiments to compare standard contexts (word windows, sentence windows) with contexts provided by a dependency parser (syntactic context) and a logical analyzer (logico-semantic context).The approach presented here relies on a dependency grammar for the syntactic representations.We also use a pattern knowledge base over the syntactic dependencies to extract flat predicative logical representations.These representations (syntactic and logical) are then used to build context vectors that are exploited in the WSD process.Various state-of-the-art algorithms including Simplified Lesk, Banerjee and Pedersen and frequency of co-occurrences are tested with these syntactic and logical contexts.Preliminary results show that defining context vectors based on these features may improve WSD by comparison with classical word and sentence context windows.However, future experiments are needed to provide more evidence over these issues.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.017 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".