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.

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

Publications

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Identifying Argumentative Discourse Structures in Persuasive Essays

Christian Stab, Iryna Gurevych
In: Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), p. 46-56, October 2014
Association for Computational Linguistics
[Online-Edition: www.ukp.tu-darmstadt.de/data/argumentation-mining/argument-annotated-essays/]
[Inproceedings]

Hierarchy Identification for Automatically Generating Table-of-Contents

Nicolai Erbs, Iryna Gurevych, Torsten Zesch
In: Proceedings of 9th Conference on Recent Advances in Natural Language Processing (RANLP 2013), p. 252-260, September 2013
INCOMA Ltd.
[Inproceedings]

Bringing Order to Digital Libraries: From Keyphrase Extraction to Index Term Assignment

Nicolai Erbs, Iryna Gurevych, Marc Rittberger
In: D-Lib Magazine, Vol. 19, p. 1-16, September 2013
[Online-Edition: http://www.dlib.org/dlib/september13/erbs/09erbs.print.html]
[Article]

A Lightweight Framework for Reproducible Parameter Sweeping in Information Retrieval

Richard Eckart de Castilho, Iryna Gurevych
In: Proceedings of the 2011 workshop on Data infrastructurEs for supporting information retrieval evaluation, p. 7-10, 2011
ACM
[Online-Edition: https://dkpro.github.io/dkpro-lab/]
[Inproceedings]
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