@inproceedings{anand-etal-2019-midas,
title = "{MIDAS} at {S}em{E}val-2019 Task 9: Suggestion Mining from Online Reviews using {ULMF}it",
author = "Anand, Sarthak and
Mahata, Debanjan and
Aggarwal, Kartik and
Mehnaz, Laiba and
Shahid, Simra and
Zhang, Haimin and
Kumar, Yaman and
Shah, Rajiv and
Uppal, Karan",
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/S19-2213",
doi = "10.18653/v1/S19-2213",
pages = "1213--1217",
abstract = "In this paper we present our approach to tackle the Suggestion Mining from Online Reviews and Forums Sub-Task A. Given a review, we are asked to predict whether the review consists of a suggestion or not. Our model is based on Universal Language Model Fine-tuning for Text Classification. We apply various pre-processing techniques before training the language and the classification model. We further provide analysis of the model. Our team ranked 10th out of 34 participants, achieving an F1 score of 0.7011.",
}
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%0 Conference Proceedings
%T MIDAS at SemEval-2019 Task 9: Suggestion Mining from Online Reviews using ULMFit
%A Anand, Sarthak
%A Mahata, Debanjan
%A Aggarwal, Kartik
%A Mehnaz, Laiba
%A Shahid, Simra
%A Zhang, Haimin
%A Kumar, Yaman
%A Shah, Rajiv
%A Uppal, Karan
%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 anand-etal-2019-midas
%X In this paper we present our approach to tackle the Suggestion Mining from Online Reviews and Forums Sub-Task A. Given a review, we are asked to predict whether the review consists of a suggestion or not. Our model is based on Universal Language Model Fine-tuning for Text Classification. We apply various pre-processing techniques before training the language and the classification model. We further provide analysis of the model. Our team ranked 10th out of 34 participants, achieving an F1 score of 0.7011.
%R 10.18653/v1/S19-2213
%U https://aclanthology.org/S19-2213
%U https://doi.org/10.18653/v1/S19-2213
%P 1213-1217
Markdown (Informal)
[MIDAS at SemEval-2019 Task 9: Suggestion Mining from Online Reviews using ULMFit](https://aclanthology.org/S19-2213) (Anand et al., SemEval 2019)
ACL
- Sarthak Anand, Debanjan Mahata, Kartik Aggarwal, Laiba Mehnaz, Simra Shahid, Haimin Zhang, Yaman Kumar, Rajiv Shah, and Karan Uppal. 2019. MIDAS at SemEval-2019 Task 9: Suggestion Mining from Online Reviews using ULMFit. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1213–1217, Minneapolis, Minnesota, USA. Association for Computational Linguistics.