Exploration of Word Embeddings with Graph-Based Context Adaptation for Enhanced Word Vectors
Bibliographic record
Abstract
In the aspect of information storage, text assumes a central role, necessitating streamlined and effective methods for swift retrieval. Among various text representations, the vector form stands out for its remarkable efficiency, especially when dealing with expansive datasets. Arranging words that are similar in meaning close to each other in the vectorized representation helps improve how well the system performs in different Natural Language Processing related tasks. Previous methods, primarily centered on capturing word context through neural language models, have fallen short in delivering high scores for word similarity problems. This paper investigates the connection between representing words in vector form and the improved performance and accuracy observed in Natural Language Processing tasks. It introduces a method to represent words as a graph, aiming to preserve their inherent relationships and to enhance overall capabilities in semantic representation. Experimental deployment of this technique across diverse text corpora underscores its superiority over conventional word embedding approaches. The findings contribute to the evolving landscape of semantic representation learning but also illuminates their implications for text classification tasks, especially within the context of dynamic embedding models.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".