@inproceedings{zhou-etal-2019-zqm,
title = "{ZQM} at {S}em{E}val-2019 Task9: A Single Layer {CNN} Based on Pre-trained Model for Suggestion Mining",
author = "Zhou, Qimin and
Zhang, Zhengxin and
Wu, Hao and
Wang, Linmao",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2226",
doi = "10.18653/v1/S19-2226",
pages = "1287--1291",
abstract = "This paper describes our system that competed at SemEval 2019 Task 9 - SubTask A: {''}Sug- gestion Mining from Online Reviews and Forums{''}. Our system fuses the convolutional neural network and the latest BERT model to conduct suggestion mining. In our system, the input of convolutional neural network is the embedding vectors which are drawn from the pre-trained BERT model. And to enhance the effectiveness of the whole system, the pre-trained BERT model is fine-tuned by provided datasets before the procedure of embedding vectors extraction. Empirical results show the effectiveness of our model which obtained 9th position out of 34 teams with F1 score equals to 0.715.",
}
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<abstract>This paper describes our system that competed at SemEval 2019 Task 9 - SubTask A: ”Sug- gestion Mining from Online Reviews and Forums”. Our system fuses the convolutional neural network and the latest BERT model to conduct suggestion mining. In our system, the input of convolutional neural network is the embedding vectors which are drawn from the pre-trained BERT model. And to enhance the effectiveness of the whole system, the pre-trained BERT model is fine-tuned by provided datasets before the procedure of embedding vectors extraction. Empirical results show the effectiveness of our model which obtained 9th position out of 34 teams with F1 score equals to 0.715.</abstract>
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%0 Conference Proceedings
%T ZQM at SemEval-2019 Task9: A Single Layer CNN Based on Pre-trained Model for Suggestion Mining
%A Zhou, Qimin
%A Zhang, Zhengxin
%A Wu, Hao
%A Wang, Linmao
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F zhou-etal-2019-zqm
%X This paper describes our system that competed at SemEval 2019 Task 9 - SubTask A: ”Sug- gestion Mining from Online Reviews and Forums”. Our system fuses the convolutional neural network and the latest BERT model to conduct suggestion mining. In our system, the input of convolutional neural network is the embedding vectors which are drawn from the pre-trained BERT model. And to enhance the effectiveness of the whole system, the pre-trained BERT model is fine-tuned by provided datasets before the procedure of embedding vectors extraction. Empirical results show the effectiveness of our model which obtained 9th position out of 34 teams with F1 score equals to 0.715.
%R 10.18653/v1/S19-2226
%U https://aclanthology.org/S19-2226
%U https://doi.org/10.18653/v1/S19-2226
%P 1287-1291
Markdown (Informal)
[ZQM at SemEval-2019 Task9: A Single Layer CNN Based on Pre-trained Model for Suggestion Mining](https://aclanthology.org/S19-2226) (Zhou et al., SemEval 2019)
ACL