Skip to Main content Skip to Navigation
Theses

Fouille de motifs et modélisation statistique pour l'extraction de connaissances textuelles

Abstract : In natural language processing, two main approaches are used : machine learning and data mining. In this context, cross-referencing data mining methods based on patterns and statistical machine learning methods is apromising but hardly explored avenue. In this thesis, we present three major contributions: the introduction of delta-free patterns, used as statistical model features; the introduction of a semantic similarity constraint for the mining, calculated using a statistical model; and the introduction of sequential labeling rules, created from the patterns and selected by a statistical model.
Keywords : Features pattern
Complete list of metadata

Cited literature [103 references]  Display  Hide  Download

https://tel.archives-ouvertes.fr/tel-02888696
Contributor : Abes Star :  Contact
Submitted on : Friday, July 3, 2020 - 11:34:25 AM
Last modification on : Sunday, July 5, 2020 - 3:30:25 AM
Long-term archiving on: : Thursday, September 24, 2020 - 7:31:50 AM

File

edgalilee_th_2018_holat.pdf
Version validated by the jury (STAR)

Identifiers

  • HAL Id : tel-02888696, version 1

Citation

Pierre Holat. Fouille de motifs et modélisation statistique pour l'extraction de connaissances textuelles. Modélisation et simulation. Université Sorbonne Paris Cité, 2018. Français. ⟨NNT : 2018USPCD045⟩. ⟨tel-02888696⟩

Share

Metrics

Les métriques sont temporairement indisponibles