GermEval 2017

GermEval-2017 : Shared Task on Aspect-based Sentiment in Social Media Customer Feedback

UKP participated in the GermEval-2017 : Shared Task on Aspect-based Sentiment in Social Media Customer Feedback. We participated in all four subtasks, namely relevance classification, document-level sentiment classification, aspect-category and sentiment detection, and aspect target extraction. The provided data contains customer feedback about Deutsche Bahn AG and was crawled from various web-sources.

We used sentence embeddings and an ensemble of classifiers for two sub-tasks as well as state-of-the-art sequence taggers for two other sub-tasks. First is a modified version of the stacked learner which has shown good performance for the SemEval 2017 Task 10, and second a BiLSTM-CRF using word- and character-level embeddings. Our systems achieved top ranks in two subtasks and placed mid-rank in the other two subtasks.

Further information on the task and the original dataset can be found on the competition website.

You can find the system description here:

UKP TU-DA at GermEval 2017: Deep Learning for Aspect Based Sentiment Detection

The source code and generated training data can be downloaded from here:

https://github.com/UKPLab/germeval2017-sentiment-detection