Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».