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

2020-12-01 · SEMEVAL 2020 · Weilong Chen, Jipeng Li, Chenghao Huang, Wei Bai, Yanru Zhang, Yan Wang

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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regression Sentiment Analysis text-classification Text Classification

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kdehumor at semeval-2020 task 7: a neural network model for detecting funniness in dataset humicroedit

2021-05-11 · SEMEVAL 2020 · Rida Miraj, Masaki Aono

This paper describes our contribution to SemEval-2020 Task 7: Assessing Humor in Edited News Headlines. Here we present a method based on a deep neural network. In recent years, quite some attention has been devoted to h…

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