Efficient simulation experiments for large-scale parameter optimisation of machine learning approaches in natural language processing

Third party funded individual grant


Project Details

Project leader:
Prof. Dr. Stefan Evert

Project members:
Prof. Dr. Stefan Evert
Sebastian Wankerl

Contributing FAU Organisations:
Lehrstuhl für Korpus- und Computerlinguistik
Professur für Korpuslinguistik

Funding source: Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst (ab 10/2013)
Acronym: E-SPar
Start date: 01/10/2016
End date: 30/09/2017


Research Fields

Methodological foundations of corpus research and digital humanities
Lehrstuhl für Korpus- und Computerlinguistik


Abstract (technical / expert description):


Our aim is to develop optimized implementations of memory-intensive machine learning approaches in natural language processing, which can be deployed on HPC clusters in order to carry out simulation experiments for the systematic optimization of model parameters. This case study focuses on matrix factorization and deep learning methods in distributional semantics.



Last updated on 2018-06-11 at 17:46