EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION
Steffen Eger, Erik-Lân Do Dinh, Ilia Kuznetsov, Masoud Kiaeeha, Iryna Gurevych · 2017
This paper describes our approach to the SemEval 2017 Task 10: "Extracting Keyphrases and Relations from Scientific Publications", specifically to Subtask (B): "Classification of identified keyphrases".We explored three different deep learning approaches: a character-level convolutional neural network (CNN), a stacked learner with an MLP meta-classifier, and an attention based Bi-LSTM.From these approaches, we created an ensemble of differently hyper-parameterized systems, achieving a micro-F 1 -score of 0.63 on the test data.Our approach ranks 2nd (score of 1st placed system: 0.64) out of four according to this official score.However, we erroneously trained 2 out of 3 neural nets (the stacker and the CNN) on only roughly 15% of the full data, namely, the original development set.When trained on the full data (training+development), our ensemble has a micro-F 1 -score of 0.69.Our code is available from https://github.com/UKPLab/semeval2017-scienceie.