Wei Jin


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The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification
Haochen Liu | Wei Jin | Hamid Karimi | Zitao Liu | Jiliang Tang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Incorporating Emoji Descriptions Improves Tweet Classification
Abhishek Singh | Eduardo Blanco | Wei Jin
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Tweets are short messages that often include specialized language such as hashtags and emojis. In this paper, we present a simple strategy to process emojis: replace them with their natural language description and use pretrained word embeddings as normally done with standard words. We show that this strategy is more effective than using pretrained emoji embeddings for tweet classification. Specifically, we obtain new state-of-the-art results in irony detection and sentiment analysis despite our neural network is simpler than previous proposals.


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HCAMiner: Mining Concept Associations for Knowledge Discovery through Concept Chain Queries
Wei Jin | Xin Wu
Coling 2010: Demonstrations