DKPro Lab

DKPro Lab is a lightweight framework for parameter sweeping experiments. It allows to set up experiments consisting of multiple interdependent tasks in a declarative manner with minimal overhead. Parameters are injected into tasks using via annotated class fields. Data produced by a task for any particular parameter configuration is stored and re-used whenever possible to avoid the needless recalculation of results. Reports can be attached to each task to post-process the experimental results and present them in a convenient manner, e.g. as tables or charts.


The source code is provided under the Apache Software License (ASL) version 2.


Displaying results 1 to 3 out of 3

Hierarchy Identification for Automatically Generating Table-of-Contents
Nicolai Erbs and Iryna Gurevych and Torsten Zesch
In: Galia Angelova and Kalina Bontcheva and Ruslan Mitkov: Proceedings of 9th Conference on Recent Advances in Natural Language Processing (RANLP 2013), p. 252-260, INCOMA Ltd., September 2013. ISSN 1313-8502.

Bringing Order to Digital Libraries: From Keyphrase Extraction to Index Term Assignment
Nicolai Erbs and Iryna Gurevych and Marc Rittberger
In: D-Lib Magazine, vol. 19, no. 9/10, p. 1-16, September 2013.

A Lightweight Framework for Reproducible Parameter Sweeping in Information Retrieval
Richard Eckart de Castilho and Iryna Gurevych
In: Maristella Agosti and Nicola Ferro and Costantino Thanos: DESIRE '11, Proceedings of the 2011 workshop on Data infrastructurEs for supporting information retrieval evaluation, p. 7-10, ACM, October 2011. ISBN 978-1-4503-0952-3.

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