DBMS-KU at SemEval-2019 Task 9: Exploring Machine Learning Approaches in Classifying Text as Suggestion or Non-Suggestion

Tirana Fatyanosa, Al Hafiz Akbar Maulana Siagian, Masayoshi Aritsugi


Abstract
This paper describes the participation of DBMS-KU team in the SemEval 2019 Task 9, that is, suggestion mining from online reviews and forums. To deal with this task, we explore several machine learning approaches, i.e., Random Forest (RF), Logistic Regression (LR), Multinomial Naive Bayes (MNB), Linear Support Vector Classification (LSVC), Sublinear Support Vector Classification (SSVC), Convolutional Neural Network (CNN), and Variable Length Chromosome Genetic Algorithm-Naive Bayes (VLCGA-NB). Our system obtains reasonable results of F1-Score 0.47 and 0.37 on the evaluation data in Subtask A and Subtask B, respectively. In particular, our obtained results outperform the baseline in Subtask A. Interestingly, the results seem to show that our system could perform well in classifying Non-suggestion class.
Anthology ID:
S19-2208
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:
1185–1191
Language:
URL:
https://aclanthology.org/S19-2208
DOI:
10.18653/v1/S19-2208
Bibkey:
Cite (ACL):
Tirana Fatyanosa, Al Hafiz Akbar Maulana Siagian, and Masayoshi Aritsugi. 2019. DBMS-KU at SemEval-2019 Task 9: Exploring Machine Learning Approaches in Classifying Text as Suggestion or Non-Suggestion. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1185–1191, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
DBMS-KU at SemEval-2019 Task 9: Exploring Machine Learning Approaches in Classifying Text as Suggestion or Non-Suggestion (Fatyanosa et al., SemEval 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/auto-file-uploads/S19-2208.pdf
Code
 TiraNosa/Text-Classification-using-VLCGA-NB +  additional community code