Ferryman at SemEval-2020 Task 7: Ensemble Model for Assessing Humor in Edited News Headlines

Weilong Chen, Jipeng Li, Chenghao Huang, Wei Bai, Yanru Zhang, Yan Wang


Abstract
Natural language processing (NLP) has been applied to various fields including text classification and sentiment analysis. In the shared task of assessing the funniness of edited news headlines, which is a part of the SemEval 2020 competition, we preprocess datasets by replacing abbreviation, stemming words, then merge three models including Light Gradient Boosting Machine (LightGBM), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representation from Transformer (BERT) by taking the average to perform the best. Our team Ferryman wins the 9th place in Sub-task 1 of Task 7 - Regression.
Anthology ID:
2020.semeval-1.131
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venues:
COLING | SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
1008–1012
Language:
URL:
https://aclanthology.org/2020.semeval-1.131
DOI:
10.18653/v1/2020.semeval-1.131
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.131.pdf