ADCSA-WSD: Adapted Discrete Crow Search Algorithm for Word Sense Disambiguation
Bibliographic record
Abstract
In the field of natural language processing, the semantic disambiguation of words is beneficial to several applications, which helps us to identify the correct meaning of a word or a sequence of words according to the given context. It can be formulated as a combinatorial optimization problem where the goal is to find the set of meanings that contribute to improving the semantic relationship between target words. The Crow Search Algorithm (CSA) is a nature-inspired algorithm. It mimics the food foraging behavior of crow birds and their social interaction. CSA can deal with both continuous and discrete optimization problems. In this paper, the Word Sense Disambiguation (WSD) is modelled as a combinatorial optimization problem that is by nature a discrete problem. For this propose the discrete version of CSA has been adapted for solving the WSD problem and a DCSA-based WSD approach is proposed and called ADCSA-WSD. The proposed approach has been evaluated and compared with state-of-the-art approaches using three well-known benchmark datasets (SemCor 3.0, SensEval-02, SensEval-03). Experimental results show that ADCSA-WSD approach is performing better than other approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".