@inproceedings{eger-etal-2017-eelection,
title = "{EELECTION} at {S}em{E}val-2017 Task 10: Ensemble of n{E}ural Learners for k{E}yphrase {C}lassifica{TION}",
author = "Eger, Steffen and
Do Dinh, Erik-L{\^a}n and
Kuznetsov, Ilia and
Kiaeeha, Masoud and
Gurevych, Iryna",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2163",
doi = "10.18653/v1/S17-2163",
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 \url{https://github.com/UKPLab/semeval2017-scienceie}.",
}
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<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-F₁-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-F₁-score of 0.69. Our code is available from https://github.com/UKPLab/semeval2017-scienceie.</abstract>
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%0 Conference Proceedings
%T EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION
%A Eger, Steffen
%A Do Dinh, Erik-Lân
%A Kuznetsov, Ilia
%A Kiaeeha, Masoud
%A Gurevych, Iryna
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F eger-etal-2017-eelection
%X 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-F₁-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-F₁-score of 0.69. Our code is available from https://github.com/UKPLab/semeval2017-scienceie.
%R 10.18653/v1/S17-2163
%U https://aclanthology.org/S17-2163
%U https://doi.org/10.18653/v1/S17-2163
%P 942-946
Markdown (Informal)
[EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION](https://aclanthology.org/S17-2163) (Eger et al., SemEval 2017)
ACL