Assessing Semantic Similarity Measures and Proposing a WuP-Resnik Hybrid Metric for Enhanced Arabic Language Processing
Bibliographic record
Abstract
The accurate quantification of semantic similarity among Arabic words presents a significant challenge in Natural Language Processing (NLP).This is a critical aspect for a wide array of text-centric applications, including recommendation systems, plagiarism detection, and information retrieval.Enhanced performance in searches and classification is achieved by simplifying concepts within machine processing and unifying words with close meanings.This research investigates the complexities of measuring semantic similarity in Arabic, a language with distinct features such as the absence of short vowels in written text that renders distinguishing words without vowel diacritics challenging for computing systems.The effectiveness of various semantic similarity metrics is meticulously evaluated in this study, with a specific focus on their applicability to Arabic WordNet and English WordNet.The challenges associated with using Arabic WordNet for measuring word similarity are illuminated, and an innovative metric, integrating the Wu-Palmer and Resnik metrics, is proposed to enhance result accuracy.The primary accomplishment of this research resides in the identification of an optimal semantic similarity metric with a reduced error rate, thereby boosting the precision of results in NLP.This pivotal advancement paves the way for more accurate semantic assessments and improved performance across a broad spectrum of applications.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".