Characterisation of language registers using emerging sequential pattern extraction

Autor: Mekki, Jade

Publikováno v: Informatique et langage [cs.CL]. Rennes 1, 2022. Français. ⟨NNT : ⟩
Informatique et langage [cs.CL]. Université de Rennes, 2022. Français. ⟨NNT : 2022REN1S098⟩

This PhD thesis aims at automatically characterising language registers. From a linguistic point of view, our contribution is to study the potential of natural language processing techniques to extract new knowledge about the casual, neutral, and for

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::1a79a18c0e8eaa023cceec92fa35b207
https://theses.hal.science/tel-03991094/file/2022_these_caracterisation_de_registres_de_langue_par_extraction_de_motifs_sequentiels_emergents_Jade_MEKKI.pdf

Contributions à la représentation et à l'apprentissage de patrons d’hyperonymie basées sur les dépendances syntaxiques et la fouille de motifs séquentiels

Autor: Issa Alaa Aldine, Hamad

Publikováno v: Artificial Intelligence [cs.AI]. Université de Bretagne Sud, 2020. English. ⟨NNT : 2020LORIS575⟩

Hypernym relation is a semantic relationship between a specific term and a generic term of it. Approaches to extract such relations from texts have gained a large interest due to the huge availability of textual resources and their key role in ontolo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::d4c2bb5faaf217000cedd813f85d4d36
https://theses.hal.science/tel-03260151

Interaction en Analyse Visuelle Progressive. Une application à la fouille progressive de motifs séquentiels

Autor: Raveneau, Vincent

Publikováno v: Computer Science [cs]. Université de Nantes, 2020. English

The Progressive Visual Analytics (PVA) paradigm has been proposed to alleviate difficulties of Visual Analytics when dealing with large datasets or time-consuming algorithms, by using intermediate results and interactions between the human and the ru

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______2592::559a6389d8faf8d97f159f9853c4e2d2
https://tel.archives-ouvertes.fr/tel-03106201/document

Can we Take Advantage of Time-Interval Pattern Mining to Model Students Activity?

Autor: Dermy, Oriane, Brun, Armelle

Publikováno v: Journal of Educational Data Mining
Journal of Educational Data Mining, 2020, pp.163-170
Proceedings of The 13th International Conference on Educational Data Mining (EDM 2020)
Proceedings of The 13th International Conference on Educational Data Mining (EDM 2020), Jul 2020, Ifrane, Morocco

International audience; Analyzing students’ activities in their learning process is an issue that has received significant attention in the educational data mining research field. Many approaches have been proposed, including the popular sequential

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::90b9b2d02b3e5db8c10498ee89f76f31
https://hal.science/hal-04054460/file/EDM_2020.pdf

Mining complex data and biclustering using formal concept analysis

Autor: Juniarta, Nyoman

Publikováno v: Computer Science [cs]. Université de Lorraine, 2019. English. ⟨NNT : 2019LORR0199⟩

Knowledge discovery in database (KDD) is a process which is applied to possibly large volumes of data for discovering patterns which can be significant and useful. In this thesis, we are interested in data transformation and data mining in knowledge

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::966575dba80f7747b8170879a0c5a722
https://inria.hal.science/tel-02426034/document