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    <abstract>As online customer forums and product comparison sites increase their societal influence, users are actively expressing their opinions and posting their recommendations on their fellow customers online. However, systems capable of recognizing suggestions still lack in stability. Suggestion Mining, a novel and challenging field of Natural Language Processing, is increasingly gaining attention, aiming to track user advice on online forums. In this paper, a carefully designed methodology to identify customer-to-company and customer-to-customer suggestions is presented. The methodology implements a rule-based classifier using heuristic, lexical and syntactic patterns. The approach ranked at 5th and 1st position, achieving an f1-score of 0.749 and 0.858 for SemEval-2019/Suggestion Mining sub-tasks A and B, respectively. In addition, we were able to improve performance results by combining the rule-based classifier with a recurrent convolutional neural network, that exhibits an f1-score of 0.79 for subtask A.</abstract>
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%0 Conference Proceedings
%T NTUA-ISLab at SemEval-2019 Task 9: Mining Suggestions in the wild
%A Potamias, Rolandos Alexandros
%A Neofytou, Alexandros
%A Siolas, Georgios
%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 potamias-etal-2019-ntua
%X As online customer forums and product comparison sites increase their societal influence, users are actively expressing their opinions and posting their recommendations on their fellow customers online. However, systems capable of recognizing suggestions still lack in stability. Suggestion Mining, a novel and challenging field of Natural Language Processing, is increasingly gaining attention, aiming to track user advice on online forums. In this paper, a carefully designed methodology to identify customer-to-company and customer-to-customer suggestions is presented. The methodology implements a rule-based classifier using heuristic, lexical and syntactic patterns. The approach ranked at 5th and 1st position, achieving an f1-score of 0.749 and 0.858 for SemEval-2019/Suggestion Mining sub-tasks A and B, respectively. In addition, we were able to improve performance results by combining the rule-based classifier with a recurrent convolutional neural network, that exhibits an f1-score of 0.79 for subtask A.
%R 10.18653/v1/S19-2215
%U https://aclanthology.org/S19-2215
%U https://doi.org/10.18653/v1/S19-2215
%P 1224-1230

Markdown (Informal)

[NTUA-ISLab at SemEval-2019 Task 9: Mining Suggestions in the wild](https://aclanthology.org/S19-2215) (Potamias et al., SemEval 2019)

• NTUA-ISLab at SemEval-2019 Task 9: Mining Suggestions in the wild (Potamias et al., SemEval 2019)

ACL

• Rolandos Alexandros Potamias, Alexandros Neofytou, and Georgios Siolas. 2019. NTUA-ISLab at SemEval-2019 Task 9: Mining Suggestions in the wild. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1224–1230, Minneapolis, Minnesota, USA. Association for Computational Linguistics.