PT Unknown
AU Daxenberger, J
   Ferschke, O
   Gurevych, I
   Zesch, T
AU ACL 2014, 52nd Annual Meeting of the Association for Computational Linguistics, June 23-24, 2014 Baltimore, Maryland, USA
TI DKPro TC: A Java-based Framework for Supervised Learning Experiments on Textual Data
PY 2014
DI 10.3115/v1/P14-5011
LA en
AB We present DKPro TC, a framework for supervised learning experiments on textual data. The main goal of DKPro TC is to enable researchers to focus on the actual research task behind the learning problem and let the framework handle the rest. It enables rapid prototyping of experiments by relying on an easy-to-use workflow engine and standardized document preprocessing based on the Apache Unstructured Information Management Architecture (Ferrucci and Lally, 2004). It ships with standard feature extraction modules, while at the same time allowing the user to add customized extractors. The extensive reporting and logging facilities make DKPro TC experiments fully replicable.
ER