![]() ![]() 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. 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. The task consists of two subtasks: suggestion mining under single-domain (Subtask A) and cross-domain (Subtask B) settings. We present the INRIA approach to the suggestion mining task at SemEval 2019. Proceedings of the 13th International Workshop on Semantic EvaluationĪssociation for Computational Linguistics INRIA at SemEval-2019 Task 9: Suggestion Mining Using SVM with Handcrafted Features Cite (Informal): INRIA at SemEval-2019 Task 9: Suggestion Mining Using SVM with Handcrafted Features (Markov & Villemonte de la Clergerie, SemEval 2019) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: = " F1-score.", ![]() Association for Computational Linguistics. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1204–1207, Minneapolis, Minnesota, USA. INRIA at SemEval-2019 Task 9: Suggestion Mining Using SVM with Handcrafted Features. Anthology ID: S19-2211 Volume: Proceedings of the 13th International Workshop on Semantic Evaluation Month: June Year: 2019 Address: Minneapolis, Minnesota, USA Venue: SemEval SIG: SIGLEX Publisher: Association for Computational Linguistics Note: Pages: 1204–1207 Language: URL: DOI: 10.18653/v1/S19-2211 Bibkey: markov-villemonte-de-la-clergerie-2019-inria Cite (ACL): Ilia Markov and Eric Villemonte de la Clergerie. Abstract We present the INRIA approach to the suggestion mining task at SemEval 2019.
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