MIDAS at SemEval-2019 Task 9: Suggestion Mining from Online Reviews using ULMFit

Sarthak Anand, Debanjan Mahata, Kartik Aggarwal, Laiba Mehnaz, Simra Shahid, Haimin Zhang, Yaman Kumar, Rajiv Shah, Karan Uppal


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.
Anthology ID:
S19-2213
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:
1213–1217
Language:
URL:
https://aclanthology.org/S19-2213
DOI:
10.18653/v1/S19-2213
Bibkey:
Cite (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.
Cite (Informal):
MIDAS at SemEval-2019 Task 9: Suggestion Mining from Online Reviews using ULMFit (Anand et al., SemEval 2019)
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PDF:
https://aclanthology.org/S19-2213.pdf