Semi-supervised learning and opinion-oriented information extraction
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
Recently, information extraction (IE) has attracted much attention in the field of natural language processing (NLP). Technology-users are no longer satisfied with factual information extraction, so researchers have become attracted to the study of opinion-oriented IE. However, past investigations of opinion-oriented IE have used supervised learning algorithms that require large amounts of data and manually labeled training corpora - time and money restrictions that are widely recognized as a bottleneck in the use of machine learning algorithms for opinion-oriented IE. Attempting to break this bottleneck, researchers are turning to semi-supervised learning. This thesis thus focuses on three types of semi-supervised learning algorithms, namely, self-training, co-training, and graph-based methods. For self-training, we apply the value difference metric (VDM) as the selection metric and use naive Bayes and decision tree algorithms as underlying classifiers. For co-training, we propose an unsymmetrical co-training algorithm that combines an EM classifier and a self-training classifier together within an unsymmetrical structure without splitting the attribute set. For graph-based methods, we put forward a probability propagation algorithm based on the instance-attribute graph, for which there are two kinds of nodes, i.e., instance nodes and attribute nodes; and two types of messages, i.e., instance node messages and attribute node messages. The goal of the probability propagation algorithm is to propagate messages between nodes in order to balance the global and local situations and, ultimately, to smooth the graph. According to the experimental results, the new techniques and novel algorithms achieve better performances than do their corresponding opponents. Furthermore, several opinion-oriented IE tasks have been tackled by the semi-supervised learning algorithms in this thesis. We mainly focus on three tasks, that is, sentence subjectivity classification, contextual polarity recognition, and opinion entity identification. Self-training is used to solve the sentence subjectivity classification, co-training is used to deal with the contextual polarity recognition, and graph-based methods are used to tackle opinion entity recognition. The experiments have been designed to compare the performances of corresponding algorithms to their corresponding tasks. The results show that semi-supervised learning algorithms are suitable for the tasks of opinion-oriented IE.
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.
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 | 0,002 |
| Science ouverte | 0,000 | 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 découle