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Autor:
Eger, Steffen; Do Dinh, Erik-Lân; Kuznetsov, Ilia; Kiaeeha, Masoud; Gurevych, Iryna:

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

Quelle:
In: Association for Computational Linguistics (Hrsg.): 11th International Workshop on Semantic Evaluations (SemEval-2017) Stroudsburg, PA : Association for Computational Linguistics (2017) , 942-946

URL des Volltextes:
http://aclweb.org/anthology/S17-2163

Sprache:
Englisch

Dokumenttyp:
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings

Schlagwörter:
Computerlinguistik, Klassifikation, Publikation, Semantik, Textanalyse, Wissenschaft


Abstract(original):
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. (DIPF/Orig.)


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last modified Nov 11, 2016