Query Induction with Schema-Guided Pruning Strategies

Joachim Niehren 1, 2 Jérôme Champavère 1 Rémi Gilleron 1, 3 Aurélien Lemay 1, 2
2 LINKS - Linking Dynamic Data
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
3 MAGNET - Machine Learning in Information Networks
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : Inference algorithms for tree automata that define node selecting queries in unranked trees rely on tree pruning strategies. These impose additional assumptions on node selection that are needed to compensate for small numbers of annotated examples. Pruning-based heuristics in query learning algorithms for Web information extraction often boost the learning quality and speed up the learning process. We will distinguish the class of regular queries that are stable under a given schema-guided pruning strategy, and show that this class is learnable with polynomial time and data. Our learning algorithm is obtained by adding pruning heuristics to the traditional learning algorithm for tree automata from positive and negative examples. While justified by a formal learning model, our learning algorithm for stable queries also performs very well in practice of XML information extraction.
Type de document :
Article dans une revue
Journal of Machine Learning Research, Journal of Machine Learning Research, 2013, 14, pp.927−964
Liste complète des métadonnées

Littérature citée [35 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00607121
Contributeur : Joachim Niehren <>
Soumis le : vendredi 29 mars 2013 - 20:46:25
Dernière modification le : vendredi 15 janvier 2016 - 18:50:08
Document(s) archivé(s) le : dimanche 2 avril 2017 - 22:50:53

Fichier

0.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : inria-00607121, version 2

Citation

Joachim Niehren, Jérôme Champavère, Rémi Gilleron, Aurélien Lemay. Query Induction with Schema-Guided Pruning Strategies. Journal of Machine Learning Research, Journal of Machine Learning Research, 2013, 14, pp.927−964. 〈inria-00607121v2〉

Partager

Métriques

Consultations de la notice

396

Téléchargements de fichiers

171