@inproceedings{chen-etal-2020-ferryman-semeval-2020,
title = "Ferryman at {S}em{E}val-2020 Task 7: Ensemble Model for Assessing Humor in Edited News Headlines",
author = "Chen, Weilong and
Li, Jipeng and
Huang, Chenghao and
Bai, Wei and
Zhang, Yanru and
Wang, Yan",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.131",
doi = "10.18653/v1/2020.semeval-1.131",
pages = "1008--1012",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Ferryman at SemEval-2020 Task 7: Ensemble Model for Assessing Humor in Edited News Headlines
%A Chen, Weilong
%A Li, Jipeng
%A Huang, Chenghao
%A Bai, Wei
%A Zhang, Yanru
%A Wang, Yan
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 dec
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F chen-etal-2020-ferryman-semeval-2020
%X 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.
%R 10.18653/v1/2020.semeval-1.131
%U https://aclanthology.org/2020.semeval-1.131
%U https://doi.org/10.18653/v1/2020.semeval-1.131
%P 1008-1012
Markdown (Informal)
[Ferryman at SemEval-2020 Task 7: Ensemble Model for Assessing Humor in Edited News Headlines](https://aclanthology.org/2020.semeval-1.131) (Chen et al., SemEval 2020)
ACL