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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| Suche nach Titel in: | TUfind oder in Google |
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