EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION
Steffen Eger | Erik-Lân Do Dinh | Ilia Kuznetsov | Masoud Kiaeeha | Iryna Gurevych
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
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.
Graph-Based Semi-Supervised Conditional Random Fields For Spoken Language Understanding Using Unaligned Data
Mohammad Aliannejadi | Masoud Kiaeeha | Shahram Khadivi | Saeed Shiry Ghidary
Proceedings of the Australasian Language Technology Association Workshop 2014
- Mohammad Aliannejadi 1
- Shahram Khadivi 1
- Saeed Shiry Ghidary 1
- Steffen Eger 1
- Erik-Lân Do Dinh 1
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