@InProceedings{eger-EtAl:2017:SemEval,
  author    = {Eger, Steffen  and  Do Dinh, Erik-L\^{a}n  and  Kuznetsov, Ilia  and  Kiaeeha, Masoud  and  Gurevych, Iryna},
  title     = {EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {942--946},
  abstract  = {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.},
  url       = {http://www.aclweb.org/anthology/S17-2163}
}