AN EFFICIENT MODEL FOR ENHANCING TEXT CATEGORIZATION USING SENTENCE SEMANTICS
Bibliographic record
Abstract
Most of text categorization techniques are based on word and/or phrase analysis of the text. Statistical analysis of a term frequency captures the importance of the term within a document only. However, two terms can have the same frequency in there documents, but one term contributes more to the meaning of its sentences than the other term. Thus, the underlying model should identify terms that capture the semantics of text. In this case, the model can capture terms that present the concepts of the sentence, which leads to discovering the topic of the document. A new concept‐based model that analyzes terms on the sentence, document, and corpus levels rather than the traditional analysis of document only is introduced. The concept‐based model can effectively discriminate between nonimportant terms with respect to sentence semantics and terms which hold the concepts that represent the sentence meaning. A set of experiments using the proposed concept‐based model on different datasets in text categorization is conducted in comparison with the traditional models. The results demonstrate the substantial enhancement of the categorization quality using the sentence‐based, document‐based and corpus‐based concept analysis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 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".