Proceedings of the GermEval 2017 Shared Task

The proceedings can be found here.

The proceedings contain the task description, the description of the baseline system (by Eugen Ruppert, Abhishek Kumar and Chris Biemann) and the following system descriptions:

• h-da Participation at Germeval Subtask B: Document-level Polarity (Karen Schulz, Margot Mieskes and Christoph Becker)
• HU-HHU at GermEval-2017 Sub-task B: Lexicon-Based Deep Learning for Contextual Sentiment Analysis (Behzad Naderalvojoud, Behrang Qasemizadeh and Laura Kallmeyer)
• UKP TU-DA at GermEval 2017: Deep Learning for Aspect Based Sentiment Detection (Ji-Ung Lee, Steffen Eger, Johannes Daxenberger and Iryna Gurevych)
• Fasttext and Gradient Boosted Trees at GermEval-2017 Tasks on Relevance Classification and Document-level Polarity (Leonard Hövelmann and Christoph M. Friedrich)
• GermEval 2017 : Sequence based Models for Customer Feedback Analysis (Pruthwik Mishra, Vandan Mujadia and Soujanya Lanka)
• IDS-IUCL Contribution to Germeval2017 (Zeeshan Ali Sayyed, Daniel Dakota and Sandra Kübler)
• PotTS at GermEval-2017 Task~B: Document-Level Polarity Detection Using Hand-Crafted SVM and Deep Bidirectional LSTM Network (Uladzimir Sidarenka)

If you want to cite the task, please use the following reference:

@inproceedings{germevaltask2017,

title = {{GermEval 2017: Shared Task on Aspect-based Sentiment in Social Media Customer Feedback}},

author = {Michael Wojatzki and Eugen Ruppert and Sarah Holschneider and Torsten Zesch and Chris Biemann},

year = {2017},

booktitle = {Proceedings of the GermEval 2017 – Shared Task on Aspect-based Sentiment in Social Media Customer Feedback},

address={Berlin, Germany},

pages={1--12}

}

The proceedings and the data are published under a creative commons by nc (4.0) licence