SemEval

EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION

UKP participated in the SemEval-2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications. In particular, we participated in Task (B): “Classification of identified keyphrases”. The goal of this subtask was to classify identified keyphrases from scientific publications into one of the three classes “Material”, “Task”, and “Process”.

Our system is an ensemble of neural techniques: an attention-based Bi-LSTM model, a character-level convolutional neural net and a stacked learner with an MLP meta-classifier. Our system had a micro-F1-score of 0.63 and ranked second out of 5 systems, closely behind the first system with an F1-score of 0.64. Erroneously, we only used about 15% of the available training data. With the full training data our system has an F1-score of 0.69.

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

You can find the system description here:

EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION

The source code will be made available here:

https://github.com/UKPLab/semeval2017-scienceie