Bibliographic record
Abstract
Due to the daily rapid growth of the information, there are \nconsiderable needs to extract and discover valuable knowledge from \ndata sources such as the World Wide Web. Most of the common \ntechniques in text mining are based on the statistical analysis of a \nterm either word or phrase. These techniques consider documents as \nbags of words and pay no attention to the meanings of the document \ncontent. In addition, statistical analysis of a term frequency \ncaptures the importance of the term within a document only. However, \ntwo terms can have the same frequency in their documents, but one \nterm contributes more to the meaning of its sentences than the other \nterm. Therefore, there is an intensive need for a model that \ncaptures the meaning of linguistic utterances in a formal structure. \nThe underlying model should indicate terms that capture the \nsemantics of text. In this case, the model can capture terms that \npresent the concepts of the sentence, which leads to discover the \ntopic of the document. \n \nA new concept-based model that analyzes terms on the sentence, \ndocument and corpus levels rather than the traditional analysis of \ndocument only is introduced. The concept-based model can effectively \ndiscriminate between non-important terms with respect to sentence \nsemantics and terms which hold the concepts that represent the \nsentence meaning. \n \nThe proposed model consists of concept-based statistical analyzer, \nconceptual ontological graph representation, concept extractor and \nconcept-based similarity measure. The term which contributes to the \nsentence semantics is assigned two different weights by the \nconcept-based statistical analyzer and the conceptual ontological \ngraph representation. These two weights are combined into a new \nweight. The concepts that have maximum combined weights are selected \nby the concept extractor. The similarity between documents is \ncalculated based on a new concept-based similarity measure. The \nproposed similarity measure takes full advantage of using the \nconcept analysis measures on the sentence, document, and corpus \nlevels in calculating the similarity between documents. \n \n \nLarge sets of experiments using the proposed concept-based model on \ndifferent datasets in text clustering, categorization and retrieval \nare conducted. The experiments demonstrate extensive comparison \nbetween traditional weighting and the concept-based weighting \nobtained by the concept-based model. Experimental results in text \nclustering, categorization and retrieval demonstrate the substantial \nenhancement of the quality using: (1) concept-based term frequency \n(tf), (2) conceptual term frequency (ctf), (3) concept-based \nstatistical analyzer, (4) conceptual ontological graph, (5) \nconcept-based combined model. \n \n \nIn text clustering, the evaluation of results is relied on two \nquality measures, the F-Measure and the Entropy. In text \ncategorization, the evaluation of results is relied on three quality \nmeasures, the Micro-averaged F1, the Macro-averaged F1 and the Error \nrate. In text retrieval, the evaluation of results relies on three \nquality measures, the precision at 10 documents retrieved P(10), the \npreference measure (bpref), and the mean uninterpolated average \nprecision (MAP). All of these quality measures are improved when the \nnewly developed concept-based model is used to enhance the quality \nof the text clustering, categorization and retrieval.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".