INRIA at SemEval-2019 Task 9: Suggestion Mining Using SVM with Handcrafted Features

Ilia Markov, Eric Villemonte de la Clergerie

Abstract
We present the INRIA approach to the suggestion mining task at SemEval 2019. The task consists of two subtasks: suggestion mining under single-domain (Subtask A) and cross-domain (Subtask B) settings. We used the Support Vector Machines algorithm trained on handcrafted features, function words, sentiment features, digits, and verbs for Subtask A, and handcrafted features for Subtask B. Our best run archived a F1-score of 51.18% on Subtask A, and ranked in the top ten of the submissions for Subtask B with 73.30% F1-score.
Anthology ID:
S19-2211
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:
1204–1207
Language:
URL:
https://aclanthology.org/S19-2211
DOI:
10.18653/v1/S19-2211
Bibkey:
Cite (ACL):
Ilia Markov and Eric Villemonte de la Clergerie. 2019. INRIA at SemEval-2019 Task 9: Suggestion Mining Using SVM with Handcrafted Features. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1204–1207, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
INRIA at SemEval-2019 Task 9: Suggestion Mining Using SVM with Handcrafted Features (Markov & Villemonte de la Clergerie, SemEval 2019)
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PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/S19-2211.pdf
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