Jiang, Y., Petrak, J., Song, X. et al. (2 more authors) (2019) Team Bertha von Suttner at SemEval-2019 Task 4: Hyperpartisan News Detection using ELMo Sentence Representation Convolutional Network. In: Proceedings of the 13th International Workshop on Semantic Evaluation. SemEval 2019: International Workshop on Semantic Evaluation, 06-07 Jul 2019, Minneapolis, Minnesota, USA. ACL , pp. 840-844.
Abstract
This paper describes the participation of team “bertha-von-suttner” in the SemEval2019 task 4 Hyperpartisan News Detection task. Our system1 uses sentence representations from averaged word embeddings generated from the pre-trained ELMo model with Convolutional Neural Networks and Batch Normalization for predicting hyperpartisan news. The final pre- dictions were generated from the averaged pre- dictions of an ensemble of models. With this architecture, our system ranked in first place, based on accuracy, the official scoring metric.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Association for Computational Linguistics. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 24 Jun 2019 14:23 |
Last Modified: | 24 Jun 2019 14:30 |
Published Version: | https://www.aclweb.org/anthology/papers/S/S19/S19-... |
Status: | Published |
Publisher: | ACL |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:146659 |