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            <namePart type="given">Ekaterina</namePart>
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    <abstract>In this paper, we present neural model architecture submitted to the SemEval-2019 Task 9 competition: “Suggestion Mining from Online Reviews and Forums”. We participated in both subtasks for domain specific and also cross-domain suggestion mining. We proposed a recurrent neural network architecture that employs Bi-LSTM layers and also self-attention mechanism. Our architecture tries to encode words via word representation using ELMo and ensembles multiple models to achieve better results. We highlight importance of pre-processing of user-generated samples and its contribution to overall results. We performed experiments with different setups of our proposed model involving weighting of prediction classes for loss function. Our best model achieved in official test evaluation score of 0.6816 for subtask A and 0.6850 for subtask B. In official results, we achieved 12th and 10th place in subtasks A and B, respectively.</abstract>
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%0 Conference Proceedings
%T NL-FIIT at SemEval-2019 Task 9: Neural Model Ensemble for Suggestion Mining
%A Pecar, Samuel
%A Simko, Marian
%A Bielikova, Maria
%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 pecar-etal-2019-nl
%X In this paper, we present neural model architecture submitted to the SemEval-2019 Task 9 competition: “Suggestion Mining from Online Reviews and Forums”. We participated in both subtasks for domain specific and also cross-domain suggestion mining. We proposed a recurrent neural network architecture that employs Bi-LSTM layers and also self-attention mechanism. Our architecture tries to encode words via word representation using ELMo and ensembles multiple models to achieve better results. We highlight importance of pre-processing of user-generated samples and its contribution to overall results. We performed experiments with different setups of our proposed model involving weighting of prediction classes for loss function. Our best model achieved in official test evaluation score of 0.6816 for subtask A and 0.6850 for subtask B. In official results, we achieved 12th and 10th place in subtasks A and B, respectively.
%R 10.18653/v1/S19-2214
%U https://aclanthology.org/S19-2214
%U https://doi.org/10.18653/v1/S19-2214
%P 1218-1223

Markdown (Informal)

[NL-FIIT at SemEval-2019 Task 9: Neural Model Ensemble for Suggestion Mining](https://aclanthology.org/S19-2214) (Pecar et al., SemEval 2019)

• NL-FIIT at SemEval-2019 Task 9: Neural Model Ensemble for Suggestion Mining (Pecar et al., SemEval 2019)

ACL

• Samuel Pecar, Marian Simko, and Maria Bielikova. 2019. NL-FIIT at SemEval-2019 Task 9: Neural Model Ensemble for Suggestion Mining. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1218–1223, Minneapolis, Minnesota, USA. Association for Computational Linguistics.