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

Eger, Steffen ; Do Dinh, Erik-Lân ; Kuznetsov, Ilia ; Kiaeeha, Masoud ; Gurevych, Iryna (2017)
EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION.
Vancouver, Canada
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (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.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2017
Autor(en): Eger, Steffen ; Do Dinh, Erik-Lân ; Kuznetsov, Ilia ; Kiaeeha, Masoud ; Gurevych, Iryna
Art des Eintrags: Bibliographie
Titel: EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION
Sprache: Englisch
Publikationsjahr: August 2017
Verlag: Association for Computational Linguistics
Buchtitel: Proceedings of the International Workshop on Semantic Evaluation
Veranstaltungsort: Vancouver, Canada
URL / URN: http://aclweb.org/anthology/S17-2163
Zugehörige Links:
Kurzbeschreibung (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.

Freie Schlagworte: UKP_a_DLinNLP;UKP_reviewed
ID-Nummer: TUD-CS-2017-0047
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung
Hinterlegungsdatum: 28 Feb 2017 09:53
Letzte Änderung: 24 Jan 2020 12:03
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