@inproceedings{markov-villemonte-de-la-clergerie-2019-inria,
title = "{INRIA} at {S}em{E}val-2019 Task 9: Suggestion Mining Using {SVM} with Handcrafted Features",
author = "Markov, Ilia and
Villemonte de la Clergerie, Eric",
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/S19-2211",
doi = "10.18653/v1/S19-2211",
pages = "1204--1207",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T INRIA at SemEval-2019 Task 9: Suggestion Mining Using SVM with Handcrafted Features
%A Markov, Ilia
%A Villemonte de la Clergerie, Eric
%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 markov-villemonte-de-la-clergerie-2019-inria
%X 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.
%R 10.18653/v1/S19-2211
%U https://aclanthology.org/S19-2211
%U https://doi.org/10.18653/v1/S19-2211
%P 1204-1207
Markdown (Informal)
[INRIA at SemEval-2019 Task 9: Suggestion Mining Using SVM with Handcrafted Features](https://aclanthology.org/S19-2211) (Markov & Villemonte de la Clergerie, SemEval 2019)
ACL