We present a deep learning extension for the multi-purpose text classification framework DKPro Text Classification (DKPro TC). DKPro TC is a flexible framework for creating easily shareable and reproducible end-to-end NLP experiments involving machine learning. We provide an overview of the current state of DKPro TC, which does not allow integration of deep learning, and discuss the necessary conceptual extensions. These extensions are based on an analysis of common deep learning setups found in the literature to support all common text classification setups, i.e. single outcome, multi outcome, and sequence classification problems. Additionally to providing an end-to-end shareable environment for deep learning experiments, we provide convenience features that take care of repetitive steps, such as pre-processing, data vectorization and pruning of embeddings. By moving a large part of this boilerplate code into DKPro TC, the actual deep learning framework code improves in readability and lowers the amount of redundant source code considerably. As proof-of-concept, we integrate Keras, DyNet, and DeepLearning4J.
@InProceedings{HORSMANN18.45, author = {Tobias Horsmann and Torsten Zesch}, title = "{DeepTC – An Extension of DKPro Text Classification for Fostering Reproducibility of Deep Learning Experiments}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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