%0 Journal Article
%T Towards Understanding Creative Language in Tweets
%A Linrui Zhang
%A Yisheng Zhou
%A Yang Yu
%A Dan Moldovan
%J Journal of Software Engineering and Applications
%P 447-459
%@ 1945-3124
%D 2019
%I Scientific Research Publishing
%R 10.4236/jsea.2019.1211028
%X Extracting fine-grained information from social media is traditionally a challenging task, since the language used in social media messages is usually informal, with creative genre-specific terminology and expression. How to handle such a challenge so as to automatically understand the opinions that people are communicating has become a hot subject of research. In this paper, we aim to show that leveraging the pre-learned knowledge can help neural network models understand the creative language in Tweets. In order to address this idea, we present a transfer learning model based on BERT. We fine-turned the pre-trained BERT model and applied the customized model to two downstream tasks described in SemEval-2018: Irony Detection task and Emoji Prediction task of Tweets. Our model could achieve an F-score of 38.52 (ranked 1/49) in Emoji Prediction task and 67.52 (ranked 2/43) and 51.35 (ranked 1/31) in Irony Detection subtask A and subtask B. The experimental results validate the effectiveness of our idea.
%K Natural Language Processing
%K Deep Learning
%K Transfer Learning
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=96420