Publications associées à Concept mining

Scalable maximal subgraph mining with backbone-preserving graph convolutions

Karl Aberer, Thanh Trung Huynh, Quoc Viet Hung Nguyen, Thành Tâm Nguyên

Maximal subgraph mining is increasingly important in various domains, including bioinformatics, genomics, and chemistry, as it helps identify common characteristics among a set of graphs and enables their classification into different categories. Existing ...

ELSEVIER SCIENCE INC2023

Early Prediction of Conceptual Understanding in Interactive Simulations

Jade Maï L Cock, Christian Giang, Mirko Marras

Interactive simulations allow students to independently ex- plore scientific phenomena and ideally infer the underlying principles through their exploration. Effectively using such environments is challenging for many students and there- fore, adaptive gui ...

2021

Overview of CLEF HIPE 2020: Named Entity Recognition and Linking on Historical Newspapers

Maud Ehrmann, Matteo Romanello

This paper presents an overview of the first edition of HIPE (Identifying Historical People, Places and other Entities), a pioneering shared task dedicated to the evaluation of named entity processing on historical newspapers in French, German and English. ...

Springer2020

Extended Overview of CLEF HIPE 2020: Named Entity Processing on Historical Newspapers

Maud Ehrmann, Matteo Romanello

This paper presents an extended overview of the first edition of HIPE (Identifying Historical People, Places and other Entities), a pioneering shared task dedicated to the evaluation of named entity processing on historical newspapers in French, German and ...

CEUR-WS2020

Introducing the CLEF 2020 HIPE Shared Task: Named Entity Recognition and Linking on Historical Newspapers

Maud Ehrmann, Matteo Romanello

Since its introduction some twenty years ago, named entity (NE) processing has become an essential component of virtually any text mining application and has undergone major changes. Recently, two main trends characterise its developments: the adoption of ...

Springer International Publishing2020

A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience

Christian O'Reilly, Elisabetta Iavarone

The curation of neuroscience entities is crucial to ongoing efforts in neuroinformatics and computational neuroscience, such as those being deployed in the context of continuing large-scale brain modelling projects. However, manually sifting through thousa ...

Springer2019

Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques

Marcos Rubinstein, Amirhossein Mostajabi

Article Open Access Published: 08 November 2019 Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques Amirhossein Mostajabi, Declan L. Finney, Marcos Rubinstein & Farhad Rachidi npj Climate and ...

2019

Mapping Affinities in Academic Organizations

Frédéric Kaplan, Boris Beaude, Dario Rodighiero

Scholarly affinities are one of the most fundamental hidden dynamics that drive scientific development. Some affinities are actual, and consequently can be measured through classical academic metrics such as co-authoring. Other affinities are potential, an ...

2018

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Concept mining

Scalable maximal subgraph mining with backbone-preserving graph convolutions

Early Prediction of Conceptual Understanding in Interactive Simulations

Overview of CLEF HIPE 2020: Named Entity Recognition and Linking on Historical Newspapers

Extended Overview of CLEF HIPE 2020: Named Entity Processing on Historical Newspapers

Introducing the CLEF 2020 HIPE Shared Task: Named Entity Recognition and Linking on Historical Newspapers

A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience

Named Entity Processing for Historical Texts

Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques

Mapping affinities: visualizing academic practice through collaboration

Mapping Affinities in Academic Organizations

Graph Chatbot

Chattez avec Graph Search

Overview of CLEF HIPE 2020: Named Entity Recognition and Linking on Historical Newspapers

A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience

Early Prediction of Conceptual Understanding in Interactive Simulations

Mapping Affinities in Academic Organizations

Extended Overview of CLEF HIPE 2020: Named Entity Processing on Historical Newspapers

Scalable maximal subgraph mining with backbone-preserving graph convolutions

Introducing the CLEF 2020 HIPE Shared Task: Named Entity Recognition and Linking on Historical Newspapers

Named Entity Processing for Historical Texts

Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques

Mapping affinities: visualizing academic practice through collaboration