YNU_DYX at SemEval-2019 Task 9: A Stacked BiLSTM for Suggestion Mining Classification

Yunxia Ding, Xiaobing Zhou, Xuejie Zhang

Abstract
In this paper we describe a deep-learning system that competed as SemEval 2019 Task 9-SubTask A: Suggestion Mining from Online Reviews and Forums. We use Word2Vec to learn the distributed representations from sentences. This system is composed of a Stacked Bidirectional Long-Short Memory Network (SBiLSTM) for enriching word representations before and after the sequence relationship with context. We perform an ensemble to improve the effectiveness of our model. Our official submission results achieve an F1-score 0.5659.
Anthology ID:
S19-2223
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1272–1276
Language:
URL:
https://aclanthology.org/S19-2223
DOI:
10.18653/v1/S19-2223
Bibkey:
Cite (ACL):
Yunxia Ding, Xiaobing Zhou, and Xuejie Zhang. 2019. YNU_DYX at SemEval-2019 Task 9: A Stacked BiLSTM for Suggestion Mining Classification. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1272–1276, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
YNU_DYX at SemEval-2019 Task 9: A Stacked BiLSTM for Suggestion Mining Classification (Ding et al., SemEval 2019)
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PDF:
https://preview.aclanthology.org/nschneid-patch-1/S19-2223.pdf
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